FCM Model Research Articles

Simulation of maize crop growth using an improved crop model considering the disintegrated area of biodegradable film

• An improved crop growth model (FCM) under biodegradable film mulching was developed. • FCM model performed well in capturing the temporal trends in crop growth dynamics. • Biodegradable film mulching results in a slight decrease of crop yield. • An irrigation depth of 22.5 mm as optimal treatment was recommended in arid region. • Disintegrated rate should not exceed 63 % and 38 % in 2018 and 2019, respectively. Biodegradable film is recognized as a promising alternative to plastic film within crop mulching. This is because biodegradable film disintegrates into water and carbon dioxide through soil microorganisms-facilitated catalytic decomposition during the crop growth season. However, biodegradable film mulching (BFM) also has some disadvantages, such as the water and heat conservation being poor during nighttime period. Crop growth and yield is reduced by a suboptimal distribution of soil water and temperature. Therefore, there is value in assessing the effects of BFM on crop growth during daytime and nighttime periods, and particularly under different irrigation levels. This study developed a crop growth model (FCM) considering the disintegrated area of biodegradable film mulching to capture crop growth dynamics under different soil water contents and mulching areas. The disintegrated area of biodegradable film was determined by an image method. The model was firstly calibrated and validated using observed data collected over two years (2018 and 2019) under three mulching treatments [plastic film mulching (PFM), biodegradable film mulching (BFM), and no film mulching (NFM)] and three irrigation levels (irrigation depths of 30, 22.5, and 15 mm). The model was then used to evaluate crop growth rate ( k ), crop yield ( CY ), and water use efficiency (WUE) during daytime and nighttime periods. The results showed that the simulation accuracy of the FCM model was sufficiently high under different treatments, with a root mean square error ( RMSE) of 284.6 kg ha –1 . The benefits of BFM for crop growth were similar to that of PFM under a low range of effective accumulated soil temperature (EAST). However, differences in crop growth variables between PFM and BFM increased with an increase in EAST. Average leaf area index ( LAI ) and aboveground dry biomass ( AB ) during both years increased by 16.9 % and 39.2 % under PFM compared to that under BFM, respectively. In general, the harvest index ( HI ), CY , and water use efficiency (WUE) under BFM were lower than that under PFM. There were comparable variations in crop growth under different irrigation levels. The highest WUE occurred in the BFM 22.5 treatment at 44.5 kg ha –1 mm –1 . Therefore, this study recommends an irrigation depth of 22.5 mm as an optimal irrigation treatment in an arid region.

Case model landscapes: toward an improved representation of knowledge-intensive processes using the fCM-language

Case Management is a paradigm to support knowledge-intensive processes. The different approaches developed for modeling these types of processes tend to result in scattered information due to the low abstraction level at which the inherently complex processes are represented. Thus, readability and understandability are more challenging than in imperative process models. This paper extends a case modeling language—the fragment-based Case Management (fCM) language—to a so-called fCM landscape (fCML) with the goal of modeling a single knowledge-intensive process from a higher abstraction level. Following the Design Science Research (DSR) methodology, we first define requirements for an fCML, and then review how literature—in the fields of process overviews and case management—could support them. Design decisions are formalized by specifying a syntax for an fCML and the transformation rules from a given fCM model. The proposal is empirically evaluated via a laboratory experiment. Quantitative results imply that interpreting an fCML requires less effort in terms of time—and is thus more efficient—than interpreting its equivalent fCM case model. Qualitative results provide indications on the factors affecting case model interpretation and guidelines for future work.

Statistical analysis and fuzzy clustering model to predict limestone rock mass quality (Qsrm) and degree of karstification (D.K) using geophysical parameters (case study of some areas in western Iran)

Since rock mass classification is based on common Q classification system and its modified system for sedimentary rocks (Qsrm) requires direct access to the rock mass, indirect methods are usually used to predict Q and Qsrm. The most important methods are geophysical methods such as pressure wave velocity (Vp) and electrical resistivity (ER). Considering the characteristics of cavities and other important properties of limestones in Qsrm, this classification is a good measure of quality in these rocks. The degree of karstification (D.K), in addition to the Qsrm, is also an important parameter in these rocks. Therefore, in this study, in addition to primary statistical analysis, using fuzzy C-means clustering (FCM) method, a multi-input–multi-output model for indirect estimation of Qsrm and D.K is presented. In addition to Vp and ER, two other independent variables called DCE and Kp were used to indirect prediction of Qsrm and D.K. DCE is the difference in the error of calculating the experimental relationships in estimating Q and Qsrm using Vp and ER, and Kp represents the ratio of Vp in the field to its value in the laboratory (Kp = VPLab/VPField). The proposed model has the ability to delete any of the mentioned independent variables in the absence of sufficient data except Vp. R2, RMSE and VAF evaluation indicators are highly consistent with the FCM model performance and when all four independent variables are used in the input data, we will have the best efficiency of the model.

The Model of How Languages will Develop

The total number of speakers of a language may increase or decrease over time because of various and complicated influences. In this paper, we build the quantitative prediction model based on gray estimation, FCM model and geographical distribution prediction model to predict the distribution of language users. In addition, we conducted a sensitivity analysis of the model to facilitate future research.

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

Predicting and reducing blast-induced ground vibrations is a common concern among engineers and mining enterprises. Dealing with these vibrations is a challenging issue as they may result in the instability of the surrounding structures, highways, water pipes, railways, and residential areas. In this study, the effects of blasting in a quarry mine in Vietnam were examined. A total of 25 blasting events were investigated with the help of an unmanned aerial vehicle, micromate instruments, and blast patterns, and 83 observations were recorded. Subsequently, the fuzzy C-means clustering (FCM) algorithm was applied to classify the 83 observations based on the blast parameters. Finally, based on the classification of the blasts, quantile regression neural network (QRNN) models were developed. The combination of FCM and QRNN models resulted in a novel, hybrid model (FCM-QRNN) for predicting blast-induced ground vibration. The US Bureau of Mines (USBM), random forest (RF), QRNN (without clustering), and artificial neural network (ANN) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model. The results indicate that the proposed FCM-QRNN model has a higher accuracy than the other models: USBM, QRNN, RF, and ANN. The proposed model can be used to control the undesirable effects of blast-induced ground vibration. Although this study and the proposed FCM-QRNN model are original works with positive results, the performance of this model in other locations still needs to be considered as a case study for further scientific information.

Enhancing image quality of ghost imaging by fuzzy c-means clustering method

We presented a novel ghost imaging scheme based on fuzzy c-means clustering (FCM) to reduce measurements and improve the visibility of the reconstruction image. Different from the GI methods, the FCM model is first employed to partition the intensity values of the reference light path and probe light path. Then, the relative speckle patterns and bucket intensity values are selected with respect to the clustering results. Finally, the object can be obtained by conventional GI methods. From the considerable simulations and experimental results, we conclude that the proposed scheme can enhance the visibility of the reconstruction image by using much fewer data from measurements compared with the existing GI methods.

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.

Investigating the Impact of Mobility Models on MANET Routing Protocols

A mobile ad hoc network (MANET) is a type of multi-hop network under different movement patterns without requiring any fixed infrastructure or centralized control. The mobile nodes in this network moves arbitrarily and topology changes frequently. In MANET routing, protocols play an important role to make reliable communication between nodes. There are several issues affecting the performance of MANET routing protocols. Mobility is one of the most significant factors that has an impact on the routing process. In this paper, FCM, SCM, RWM and HWM mobility models are designed to analyze the performance of AODV, OLSR and GRP protocols, with ten pause time values. These models are based on varying speeds and pause time of MANET participants. Different node parameters such as data drop rate, average end-to-end delay, media access delay, network load, retransmission attempts and throughput are used to make a performance comparison between mobility models. The simulation results showed that in most of the cases OLSR protocol provides better performance than other two routing protocols and it is more suitable for networks that require low delay and retransmission attempts, and high throughput.

Research on success strategy of Smart Rehabilitation device cluster - Applying FCM model

Research on success strategy of Smart Rehabilitation device cluster - Applying FCM model

Fuzzy cognitive maps as a decision support tool for container transport logistics

In this paper we present Fuzzy Cognitive Maps, a well established decision making technique that combines Artificial Neural Networks and Fuzzy Logic, for making decisions regarding Container Transport Logistics. A Fuzzy Cognitive Map is created based on the knowledge extracted by a domain expert. Based on this knowledge, a model of the interactions and causal relations among various key Logistics factors is created. The evolution of this Fuzzy Cognitive Map, imitates the cognitive processes of a decision maker during reasoning. Having the FCM created, it is examined both statically and dynamically. A number of scenarios are introduced and the decision making capabilities of the technique are presented by simulating these scenarios and finding the predicted outcomes according to the evolution of the FCM model and the expert’s knowledge. FCM’s predicted consequences of specific decisions can be valuable to decision makers since they can test their decisions and proceed with them only if the results are desirable.

Potential changes to travel behaviors & patterns: a fuzzy cognitive map modeling approach

The future of travel will be affected by a number of disruptive changes, including advancements in vehicle technology, such as automated vehicles, changes in population demographics and the economy, and lifestyle changes. It is difficult to say just how much each change will affect the amount and type of travel in the future, especially given the amount of uncertainty there is regarding the trajectory of these changes and their effects. The authors examined changes that are likely to affect transportation behaviors in the future, developed a “fuzzy cognitive map” (FCM) of the relationships, and used the FCM model to investigate the effects of those relationships. The results of the study show that FCM models offer a promising method for transportation planners to enhance their ability to reason about system effects when quantitative information is limited and uncertain. More specifically, the results provide some initial guidance on the potential impacts of disruptive changes on future travel, which may help in targeting limited research funds on the most consequential potential changes.

Radial basis function network using intuitionistic fuzzy C means for software cost estimation

Software development has become an important activity for many modern organisations. Software engineers have become more and more concerned about accurately predicting the cost and quality of software product under development. In the last few decades many software cost estimation models have been developed but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper we propose the use of Radial Basis Function Network RBFN for software cost estimation using Intuitionistic Fuzzy C Means IFCM with Gaussian potential functions. This technique selects the most desirable cluster centres, thereby increasing the clustering accuracy which results in improved software cost estimations. A comparison of RBFN using IFCM, Fuzzy C Means FCM and conventional COCOMO model is presented. The datasets used in our study are the COCOMO81 dataset and NASA93 dataset. Experimental results are given to show the effectiveness of the proposed method.

Robust Implementation of ALFIS for Prediction of Medical Information System

The institute of medicine has long recognized Problems with health care quality and for more than a decade has advocated using health information technology to improve quality. The fuzzy cognitive map has gradually emerged as a concrete paradigm for knowledge representations, and simulation conditions are applicable, to numerous research and application field. However we have proposed efficient methods to determine clustering based investigated systems, for medical decision making. The manually developed models have a substantially shortcoming due to difficulties in assessing its reliability. In this paper we proposed a fuzzy logical network that enhances the learning ability of FCM. Our approach discusses the inference mechanisms of conventional FCM in the determination of membership functions. FCM models of investigations system can be automatically constructed from medical data using our approach. In the employed fuzzy logical networks the concept of mutual subset used to describe the casualties which provide more transparent interpretations in FCM. The effectiveness of the proposed approach in prediction of jaundice using clustering is demonstrated through numerical simulation. in FCM. The effectiveness of the proposed approach in prediction of jaundice using clustering is demonstrated through numerical simulation interpretations in FCM. The effectiveness of the proposed approach in prediction of jaundice using clustering is demonstrated through numerical simulation interpretations in FCM.effectivof

Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping

BackgroundMeningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition.MethodsFuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team.ResultsThe paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians’ decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results with sensitivity of 83.3% and specificity of 80%.ConclusionsThis work suggests that the application and development of a knowledge based system, using the formalization of FCMs for understanding the symptoms and causes of meningitis in children and infants, can provide a reliable front-end decision-making tool to better assist physicians.

Road Extraction Based on the Algorithms of K-Means Clustering and Hybrid Model of SVM and FCM

Information extraction is the prerequisite of remote sensing image segmentation, which is the key procedure of image analysis. In this paper hard C-means and fuzzy C-means is adopted for segmentation in remote sensing image to realize our road extraction. Firstly, we proposed k-means for image segmentation using non-supervised clustering, and we can achieve our aim finally. Meanwhile, SVM combined with Fuzzy C means was proposed and this model was implemented in remote sensing image segmentation to extract the road net. Finally the comparison with two proposed algorithm was carried out, and after experiment, SVM plus FCM model is much more accurate than k-means.

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.

A New Scatter Correction Method for KV and MV Cone Beam CT

Kilo-voltage(KV) and mega-voltage(MV) cone beam computed tomography (CBCT) play an important role in image-guided adaptive radiotherapy. However, the cupping effect, introduced by Compton scatter contribution due to a large cone angle of CBCT acquisition, significantly degrades the CBCT image quality and hampers its clinical application. The objective of this study is to develop a new CBCT scatter correction method in the image domain. The hypotheses of the proposed method are that the image intensity of different kind of materials in CBCT image should be globally uniform and the cupping effect can be described as a bias field, a low frequency signal across the whole 3-D images. Based on above assumptions, we proposed a maximum a posteriori probability (MAP) framework to estimate the bias field contribution. The well-known fuzzy C-mean model is extended to define the likelihood function of the CBCT image. A markov random field model is used to describe the prior probability of the mixture defined in FCM model. The bias field is characterized by a Gaussian prior probability with a banded covariance matrix representing a low-pass filter. An iterated conditional mode (ICM)-like approach is utilized to minimize the objective function. The corrected CBCT image can be obtained by subtracting the bias field from original CBCT image. The performance of correcting the cupping effect of CBCT image with the proposed MAP framework was tested using both contrast phantoms and clinical CBCT images. In the cylindrical shape-contrast phantom the higher density material is surrounded by the lower density material. In the contrast phantom study, the intensities of higher density material in both KV and MV CBCT images are even lower than that of lower density material due to the cupping effect. The corrected CBCT images showed almost same intensity distribution as the original contrast phantom images. In a real patient study, it is clearly shown that there is a shadow inside the brain soft tissue in the original brain KV and MV CBCT images. The corrected CBCT images significantly improved the uniformity of intensity distribution of brain soft tissue. The proposed image-based scatter correction method showed a promising result to reduce the cupping effect commonly encountered in KV and MV CBCT image.

SU‐GG‐I‐18: An Image‐Domain Based Cone Beam CT Scatter Correction Method

Purpose: Due to a large cone angle, scatter contribution significantly degrades the image quality of cone beam computed tomography (CBCT) by introducing the cupping effect. The objective of this study is to develop a new CBCT scatter correction method in image domain. Method and Materials: The hypotheses of the proposed method are that the image intensity of different kind of materials in CBCT image is globally uniform and the cupping effect can be described as a bias field, a low frequency signal across the whole 3‐D images. Based on above assumptions, we proposed a maximum a posteriori probability (MAP) framework to estimate the bias field contribution. The well‐known fuzzy C‐mean model is extended to define the likelihood function of the CBCT image. A markov random field model is used to describe the prior probability of the mixture defined in FCM model. The bias field is characterized by a zero mean Gaussian prior probability. An iterated conditional mode‐like approach is utilized to maximize the objective function. The corrected CBCT image can be obtained by subtracting the bias field from original CBCT image. Results: The performance of correcting the cupping effect of CBCT image with the proposed MAP framework was tested using both digital phantoms and clinical CBCT images. In the phantom studies the intensity of higher density material in the simulated CBCT images of contrast phantoms is even lower that that of lower density material due to cupping effect. The corrected CBCT image showed almost same intensity distribution as the original contrast phantom images. For real patient study, compared to the original brain CBCT image, the corrected CBCT image significantly improved the uniformity of intensity distribution of brain soft tissue. Conclusion: The proposed image‐based scatter correction method showed a promising result to reduce the cupping effect commonly encountered in CBCT image.

Decisions on Networked Learning based on Fuzzy Cognitive Maps

In this paper we present Fuzzy Cognitive Maps, a well established decision making technique based on Fuzzy Logic, for making decisions concerning Networked Learning. After long discussions and extended interviews with a Networked Learning expert, a Fuzzy Cognitive Map was created that models the key factors of Networked Learning and the various causal relationships that exist among them. The list of the concepts that were identified as playing important role in Network Learning and should appear in the FCM, are the following:Concept 1. Simplicity of Learning EnvironmentConcept 2. Collaboration (between students, between teachers, between students and teachers)Concept 3. Administrative/Technical SupportConcept 4. Course Domain (relevance to computers, suitable for techno lovers or technophobic groups)Concept 5. Institutional Policy on Networked LearningConcept 6. Size of Target GroupConcept 7. Size of ClassConcept 8. Quality of Networked LearningConcept 9. Number of Tutors per ClassConcept 10. Student Satisfaction The FCM was first examined statically. It was found that it contains a total of 252 cycles from which 123 are positive cycles and 129 are negative cycles. The existence of such a high number of cycles implies strong and long interactions between the concepts of the FCM. The density of model’s graph was calculated and found extremely high, indicating the complexity of the case. The conceptual centralities of the concepts that exist in the model were also calculated and the most central, and consequently the most important concepts of the model were found to be concept C3: “Administrative/Technical Support”, followed by concept C2: “Collaboration”. After that, dynamic studies of the FCM model are made and a number of computer simulations are performed to examine the predicted consequences of specific scenarios/decisions on Networked Learning. The scenarios imposed to FCM are the following:Scenario Description Scenario Description #1 All concepts are free to interact #2 Activation of C7: “Size of Class” is set & kept to 0.5 (moderate increase). All other concepts are free to interact. #3 Activation of C6: "Size of Target Group" is set & kept to 0.25 (low increase). All other concepts are free to interact. #4 Weight w34 is changed from 0.6 to -0.4. The way an increase to Administrative/Technical support is affecting Courses Domain towards computer related course is changed. All concepts are free to interact. #5 Activation of C1: "Simplicity of Learning Environment" is set & kept to 0.5 (moderate increase). All other concepts are free to interact. FCM was found capable of providing support by making predictions on the various scenarios that were imposed to the Networked Learning model that FCM creates. It can also be used for studying structural changes to Networked Learning, by first testing using the FCM, what these changes would cause to the various concepts of the FCM model and proceed with the changes only if the effects are the desirable. In conclusion, the capabilities of the FCM technique are presented and its use is examined for planning Network Learning and for enhancing success of learning programs.The FCM technique is identified as an important and useful tool that can assist Networked Learning Decision Makers.

Feature analysis through information granulation and fuzzy sets

Feature analysis and feature selection are fundamental pursuits in pattern recognition. We revisit and generalize an issue of feature selection by introducing a mechanism of soft (fuzzy) feature selection. The underlying idea is to consider features to be granular rather than numeric. By varying the level of granularity, we modify the level of contribution of the specific feature to the overall feature space. We admit an interval model of the features meaning that their values assume a form of numeric intervals. The intervalization of the features exhibits a clear-cut interpretation. Moreover a contribution of the features to the formation of the feature space can be easily controlled: the broader the interval, the less essential contribution of the feature to the entire feature space. In limit, when the intervals get broad enough, one may view the feature to be completely eliminated (dropped) from the feature space. The quantification of the features in terms of their importance is realized in the setting of the clustering FCM model (namely, a process of the binary or fuzzy feature selection is carried out and numerically quantified in the space of membership values generated by fuzzy clusters). As the focal point of this study concerns an interval-like form of information granules, we reveal how such feature intervalization helps approximate fuzzy sets described by any type of membership function. Detailed computations give rise to a detailed quantification of such granular features. Numerical experiments provide a comprehensive numerical illustration of the problem.