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Yang J, Han J, Wan Q, Xing S, Chen F. A novel similarity measurement for triangular cloud models based on dual consideration of shape and distance. PeerJ Comput Sci 2023; 9:e1506. [PMID: 37705635 PMCID: PMC10496002 DOI: 10.7717/peerj-cs.1506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 07/07/2023] [Indexed: 09/15/2023]
Abstract
It is important to be able to measure the similarity between two uncertain concepts for many real-life AI applications, such as image retrieval, collaborative filtering, risk assessment, and data clustering. Cloud models are important cognitive computing models that show promise in measuring the similarity of uncertain concepts. Here, we aim to address the shortcomings of existing cloud model similarity measurement algorithms, such as poor discrimination ability and unstable measurement results. We propose an EPTCM algorithm based on the triangular fuzzy number EW-type closeness and cloud drop variance, considering the shape and distance similarities of existing cloud models. The experimental results show that the EPTCM algorithm has good recognition and classification accuracy and is more accurate than the existing Likeness comparing method (LICM), overlap-based expectation curve (OECM), fuzzy distance-based similarity (FDCM) and multidimensional similarity cloud model (MSCM) methods. The experimental results also demonstrate that the EPTCM algorithm has successfully overcome the shortcomings of existing algorithms. In summary, the EPTCM method proposed here is effective and feasible to implement.
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Affiliation(s)
- Jianjun Yang
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
- Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chendu, Sichuan Province, China
- Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chendu, Sichuan Province, China
| | - Jiahao Han
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
| | - Qilin Wan
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
| | - Shanshan Xing
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
| | - Fei Chen
- School of Automobile and Transportation, Xihua University, Chengdu, Sichuan Province, China
- Provincial Engineering Research Center for New Energy Vehicle Intelligent Control and Simulation Test Technology of Sichuan, Xihua University, Chendu, Sichuan Province, China
- Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Xihua University, Chendu, Sichuan Province, China
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Jafarzade N, Kisi O, Yousefi M, Baziar M, Oskoei V, Marufi N, Mohammadi AA. Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources. Heliyon 2023; 9:e18415. [PMID: 37520981 PMCID: PMC10382293 DOI: 10.1016/j.heliyon.2023.e18415] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/16/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023] Open
Abstract
The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs. Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R2) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R2 of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.
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Affiliation(s)
- Naghmeh Jafarzade
- Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ozgur Kisi
- Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany
- Department of Civil Engineering, Ilia State University, 0162, Tbilisi, Georgia
| | - Mahmood Yousefi
- Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
| | - Mansour Baziar
- Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran
| | - Vahide Oskoei
- School of Life and Environmental Science, Deakin University, Geelong, Australia
| | - Nilufar Marufi
- Department of Environmental Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Mohammadi
- Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran
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Huang M, Zhang Z, Cen L, Li J, Xie J, Zhao Y. Prediction of the Number of Cumulative Pulses Based on the Photon Statistical Entropy Evaluation in Photon-Counting LiDAR. ENTROPY (BASEL, SWITZERLAND) 2023; 25:522. [PMID: 36981410 PMCID: PMC10048573 DOI: 10.3390/e25030522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/28/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Photon-counting LiDAR encounters interference from background noise in remote target detection, and the statistical detection of the accumulation of multiple pulses is necessary to eliminate the uncertainty of responses from the Geiger-mode avalanche photodiode (Gm-APD). The cumulative number of statistical detections is difficult to select due to the lack of effective evaluation of the influence of the background noise. In this work, a statistical detection signal evaluation method based on photon statistical entropy (PSE) is proposed by developing the detection process of the Gm-APD as an information transmission model. A prediction model for estimating the number of cumulative pulses required for high-accuracy ranging with the background noise is then established. The simulation analysis shows that the proposed PSE is more sensitive to the noise compared with the signal-to-noise ratio evaluation, and a minimum PSE exists to ensure all the range detections with background noise are close to the true range with a low and stable range error. The experiments demonstrate that the prediction model provides a reliable estimation of the number of required cumulative pulses in various noise conditions. With the estimated number of cumulative pulses, when the signal photons are less than 0.1 per pulse, the range accuracy of 4.1 cm and 5.3 cm are obtained under the background noise of 7.6 MHz and 5.1 MHz, respectively.
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Chen Q, Yu W, Zhao X, Nie F, Li X. Rooted Mahalanobis distance based Gustafson-Kessel fuzzy C-means. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
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Ma X, Zhao T, Guo Q, Li X, Zhang C. Fuzzy hypergraph network for recommending top-K profitable stocks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Jiao L, Yang H, Liu ZG, Pan Q. Interpretable fuzzy clustering using unsupervised fuzzy decision trees. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Prabhakaran P, Subbaiyan A, Bhaskaran P, Velusamy S. Preventive track maintenance model using fuzzy weight convolution neural network for metro rail system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In India, Rail mode of transport serves as frequently preferrable transit systems operating with the optimal cost. Typically, the Indian Railways transport thousands of people on a day-to-day basis in addition to transporting large consignment of goods. Therefore, it is important for the trains to ensure that they run on quality tracks. At times, these tracks are challenged by the friction generated by continuous passage of trains in addition to over corrosions that occur due to their environmental imbalance. Preventive Track Maintenances (PTMs) have been recently introduced by railways for enhancing the quality of railway tracks, but on the contrary, it was failed to focus on the actual needs or emergencies of railway tracks. Moreover, none of the existing methods have been tested with real time datasets. Specifically, holding only two class labels are being considered resulting in the reduction of classification performances. But the major challenging task is that the real-time datasets fall under the category of multi-variant data. Hence, this study aims to provide a Decision Support System (DSS) that predicts the Railway Track Quality (RTQ) from the real time datasets available on the track inspection data of the Indian metro rail system. The proposed research uses clustering and classification processes for achieving Predictive Track maintenance (PTM). Furthermore, the proposed method of RPTMs includes five steps namely data collection, data transformations, clustering of data, preventive maintenances, and evaluations. The undertaken datasets are transformed into numeric formats for the creation of clusters using Kernel Mean Weight Fuzzy Local Information C Means (KMWFLICMs). The resultant clusters from the data have five major types of clusters such as Normal, Low risks, Medium, High, and Emergency Risks based on the parameters of gauge, cross level attributes, turnouts and versine of mainline. From the inferred cluster results, the dataset was further classified to choose maintenance status from four major classes namely No Actions, Fixed Maintenances, Investigate Maintenances, and Emergency Maintenance pertinent to the outcomes of FWCNNs (Fuzzy Weight Convolution Neural Networks). The proposed system was experimented on MATLAB and evaluated against various machine learning approaches. Therefore, the obtained statistical results confirmed that the proposed FWCNN model had afforded higher accuracy in predicting the maintenance interventions based on relevant risk category.
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Affiliation(s)
| | - Anandakumar Subbaiyan
- Department of Civil Engineering, Perundurai, India
- Kongu Engineering College, Perundurai, India
| | - Priyanka Bhaskaran
- Department of Mechatronics Engineering, Perundurai, India
- Kongu Engineering College, Perundurai, India
| | - Sampathkumar Velusamy
- Department of Civil Engineering, Perundurai, India
- Kongu Engineering College, Perundurai, India
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Amira O, Zhang JS, Liu J. Fuzzy c-means clustering with conditional probability based K–L information regularization. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1906243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Ouafa Amira
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, People's Republic of China
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Naderipour M, Fazel Zarandi MH, Bastani S. Fuzzy community detection on the basis of similarities in structural/attribute in large-scale social networks. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09987-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.094] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Mahata N, Sing JK. A novel fuzzy clustering algorithm by minimizing global and spatially constrained likelihood-based local entropies for noisy 3D brain MR image segmentation. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106171] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A new entropy-based approach for fuzzy c-means clustering and its application to brain MR image segmentation. Soft comput 2019. [DOI: 10.1007/s00500-018-3594-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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A multistage risk decision making method for normal cloud model considering behavior characteristics. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.02.033] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Gharieb RR, Gendy G, Selim H. A Hard C-Means Clustering Algorithm Incorporating Membership KL Divergence and Local Data Information for Noisy Image Segmentation. INT J PATTERN RECOGN 2017. [DOI: 10.1142/s021800141850012x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, the standard hard C-means (HCM) clustering approach to image segmentation is modified by incorporating weighted membership Kullback–Leibler (KL) divergence and local data information into the HCM objective function. The membership KL divergence, used for fuzzification, measures the proximity between each cluster membership function of a pixel and the locally-smoothed value of the membership in the pixel vicinity. The fuzzification weight is a function of the pixel to cluster-centers distances. The used pixel to a cluster-center distance is composed of the original pixel data distance plus a fraction of the distance generated from the locally-smoothed pixel data. It is shown that the obtained membership function of a pixel is proportional to the locally-smoothed membership function of this pixel multiplied by an exponentially distributed function of the minus pixel distance relative to the minimum distance provided by the nearest cluster-center to the pixel. Therefore, since incorporating the locally-smoothed membership and data information in addition to the relative distance, which is more tolerant to additive noise than the absolute distance, the proposed algorithm has a threefold noise-handling process. The presented algorithm, named local data and membership KL divergence based fuzzy C-means (LDMKLFCM), is tested by synthetic and real-world noisy images and its results are compared with those of several FCM-based clustering algorithms.
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Affiliation(s)
- R. R. Gharieb
- Faculty of Engineering, Assiut University, Assiut 71516, Egypt
| | - G. Gendy
- El-Rajhy Liver Hospital, Assiut University, Assiut, Egypt
| | - H. Selim
- Faculty of Engineering, Assiut University, Assiut 71516, Egypt
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Chaomurilige, Yu J, Yang MS. Deterministic annealing Gustafson-Kessel fuzzy clustering algorithm. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2017.07.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhang L, Zhong W, Zhong C, Lu W, Liu X, Pedrycz W. Fuzzy C-Means clustering based on dual expression between cluster prototypes and reconstructed data. Int J Approx Reason 2017. [DOI: 10.1016/j.ijar.2017.08.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Gharieb R, Gendy G, Abdelfattah A, Selim H. Adaptive local data and membership based KL divergence incorporating C-means algorithm for fuzzy image segmentation. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.05.055] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Affiliation(s)
- Abdul Suleman
- Instituto Universitário de Lisboa (ISCTE – IUL), BRU, Lisboa, Portugal
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Zarinbal M, Fazel Zarandi MH, Turksen IB, Izadi M. A Type-2 Fuzzy Image Processing Expert System for Diagnosing Brain Tumors. J Med Syst 2015; 39:110. [PMID: 26276018 DOI: 10.1007/s10916-015-0311-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/04/2015] [Indexed: 10/23/2022]
Abstract
The focus of this paper is diagnosing and differentiating Astrocytomas in MRI scans by developing an interval Type-2 fuzzy automated tumor detection system. This system consists of three modules: working memory, knowledge base, and inference engine. An image processing method with three steps of preprocessing, segmentation and feature extraction, and approximate reasoning is used in inference engine module to enhance the quality of MRI scans, segment them into desired regions, extract the required features, and finally diagnose and differentiate Astrocytomas. However, brain tumors have different characteristics in different planes, so considering one plane of patient's MRI scan may cause inaccurate results. Therefore, in the developed system, several consecutive planes are processed. The performance of this system is evaluated using 95 MRI scans and the results show good improvement in diagnosing and differentiating Astrocytomas.
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Affiliation(s)
- M Zarinbal
- Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran,
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Chen MY, Chen BT. A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2014.09.038] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Zarinbal M, Fazel Zarandi M, Turksen I. Interval Type-2 Relative Entropy Fuzzy C-Means clustering. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.02.066] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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