Fuzzy Reasoning Model Research Articles

Risk assessment of computer network information using a proposed approach: Fuzzy hierarchical reasoning model based on scientific inversion parallel programming

Abstract Background Computer networks are involved in many fields such as business, education, marketing, government, and tourism in several forms. Technologies related to security protection and improvement of information integrity are used and developed for computer networks intruded on by unauthorized people and help save their confidentiality. Methods To improve the risk identification of computer networks, this manuscript combined a fuzzy hierarchical reasoning model with the scientific inversion parallel programming method to study the risk of computer networks. Moreover, this article defined and analyzed a d-order neighborhood message propagation algorithm. A d-order neighborhood parallel message propagation algorithm using the Gaussian graph model was proposed. Results The risk of the computer network was analyzed using the proposed method resulting in better protection effectiveness. Conclusion The simulations showed that the proposed algorithm could effectively detect risks and improve the security of the computer network.

On the impact of fuzzy-logic based BDI agent model for cyber–physical systems

Cyber–Physical Systems (CPS) interconnect embedded computing technologies into the physical world, forming a complex, multi-disciplinary, physically-effective system. However, interacting with the physical world brings unpredictability, as real-world events are uncertain and dynamic by its nature. Consequently, CPS must be capable of reasoning about these unpredictable situations and adapt their behaviour accordingly. Therefore, it is essential to enhance the intelligence of CPS to overcome this challenge effectively, leading to the development of smart CPS. As an approach, a reasoning mechanism can support the system to reason about its state and actions to handle unpredictable changes. In this study, we employ intelligent Belief-Desire-Intention (BDI) agents to achieve this objective. Nevertheless, traditional logic approaches based on crisp numbers, which are used in typical BDI agents, may not effectively handle uncertainty. Hence, the reasoning mechanism can be extended with fuzzy logic to deal with run-time uncertainties. Thus, the target system’s suitable inputs, outputs and reasoning phases are fuzzified to indicate the impact of the fuzzy logic on the CPS. To this end, an architecture is provided to deploy BDI agents integrated with a fuzzy reasoning model for handling imprecise information and improving process control. The approach is validated and evaluated on a complex case study with a heterogeneous structure controlled by multiple agents, namely the “smart production system”. The multiple modular and end-to-end experiments conducted reveal that, on the whole, the fuzzy BDI agent outperforms the classical one by up to three times, with only requiring approximately 10% more computation time.

Time-Varying Intuitionistic Fuzzy Integral for Emergency Materials Demand Prediction With Case-Based Reasoning

Recently, frequent emergencies seriously impact people’s life and production. The timely emergency material can save more people. Hence, emergency material demand prediction is a core of emergency rescue. However, emergency is a continuous, uncertain evolution process. The emergency of different periods has various situations. The time-varying and uncertain characteristics of emergency have made this representation and prediction work much more complex. In order to address this concern, this article proposes a time-varying intuitionistic fuzzy set (TVIFS) to precisely depict the time-varying scenario and the uncertainty of emergencies. For utilizing the TVIFS of different time frames, we deeply explore the integral aggregation operator of TVIFS, which can take full account of decision hesitation and possible information. Due to the different time importance of different time frames, we investigate the integral of TVIFS with constant period weight in advance. Considering the time-varying period weight, the TVIFS integral with time-varying period weight is investigated. For the sake of calculation feasibility, inspired by the relationship between continuity and dispersion, we translate the TVIFS integral with variable period weight into the TVIFS integral with constant period weight. Furthermore, we develop time-varying intuitionistic fuzzy case-based reasoning (CBR) model to forecast the emergency materials demand by designing hybrid similarity between cases. Finally, we elaborate the application of time-varying intuitionistic fuzzy CBR model by using an example of forest fire and verify the validity of our proposed method.

Q-method optimization of tunnel surrounding rock classification by fuzzy reasoning model and support vector machine

Q-method optimization of tunnel surrounding rock classification by fuzzy reasoning model and support vector machine

Solution of a pollution sensitive supply chain model under fuzzy approximate reasoning

This article develops a three-layer supply chain (SC) pollution dependent production transportation model with rework on fuzzy approximate reasoning approach. First of all, we develop a cost function of the model of reworkable items, in which pollution generates due to transportation of goods only. The aim of this study is to reduce the aggregated pollution level that simultaneously could optimize the integrated SC cost function under fuzzy approximate reasoning. Basically, the theory of two-tailed (randomized L-R fuzzy) approximate reasoning is introduced first time on demand rate in this model which is an extension of one-tailed (randomized L-fuzzy/R-fuzzy) approximate reasoning in the perspectives of the proposed model. An application of a new heart like approximate dual feasible region has also been utilized here also. However, a comparative study has been made taking numerical examples for crisp, general fuzzy, one- and two-tailed fuzzy approximate reasoning models exclusively. Finally, few numerical analyses with sensitivity and graphical discussions are done to legitimize the model.

Active vibration control of thin-walled milling based on ANFIS parameter optimization

In view of the time-varying dynamic characteristics of thin-walled milling, an optimization method of thin-walled milling control parameters based on the ANFIS system is proposed. Firstly, the control system strategy design and equipment selection construction were carried out, and the fuzzy inference model was built based on matlab platform; Subsequently, the fuzzy inference model of the thin-walled workpiece milling process was obtained by collecting fuzzy inference training data set of and the ANFIS system training. The proportional control coefficient of the controller was optimized based on the input and output relation of the model. Finally, the dynamic response and surface roughness of the controlled thin-walled panel milling process before and after optimization were obtained through cutting experiments, and the effectiveness of the ANFIS method to optimize the control parameters and the reliability of the fuzzy reasoning model were verified through analysis of the reasoning results. The results show that the ANFIS system can predict the vibration response performance of the target by setting the input and output reasonably, and optimize the related variable parameters according to the predicted results, so as to make the thin-wall milling system more stable.

Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity.

Bee Colony Optimization metaheuristic for fuzzy membership functions tuning

The successfulness of the application of fuzzy logic to real-life problems depends on a number of parameters, such as the number and shapes of fuzzy membership functions, which are usually defined upon intuition or the subjective knowledge of relevant experts. One way to improve the performance of the fuzzy reasoning model is to use heuristic or metaheuristic techniques in order to optimize the shapes of membership functions. In this paper, the Bee Colony Optimization (BCO) algorithm is applied as a tool suitable for the specified optimization problem. The BCO belongs to the group of nature-inspired metaheuristics. The purpose of this paper is to present and discuss a strategy for the adjustment of fuzzy logic membership functions using the variant of the BCO algorithm based on the improvement of complete solutions and show its real-life application to the problem of the estimation of freight train energy consumption. According to the obtained results, it can be concluded that the precision of the developed fuzzy reasoning model is significantly increased after tuning membership functions by the BCO.

An interval type-2 fuzzy reasoning model for digital transformation project risk assessment

This study presents a new risk assessment model by combining interval type-2 fuzzy best-worst method (IT2F-BWM) and perceptual reasoning for risk evaluation of digital transformation projects. Digital transformations are technology-driven change processes that transform many businesses. Digital transformations are risky and difficult undertakings so that the decision-makers should be supported with appropriate risk assessment tools. The proposed model is a step towards this direction by providing decision-makers with a practical and applicable risk assessment tool that computes with words through a transparent and interpretable reasoning mechanism. In the proposed model, the relative importance of risk factors (RFs) is derived by using IT2F-BWM. Then, IT2F rule-based model is developed by considering risk factors along with the likelihood and severity of risks. The risk magnitudes of digital transformation projects are evaluated via perceptual reasoning. Both the input and output of the reasoning model are IT2F numbers, and the resulting risk magnitudes are decoded into words such as critical, major, and minor so as to ease interpretation. A real-life digital transformation risk assessment case study is conducted in order to demonstrate the applicability of the proposed model.

Real-time detection of the interaction between an upper-limb power-assist robot user and another person for perception-assist

Assisting aged population or a population with disabilities is a critical problem in today’s world. To compensate for their declined motor function, power-assist wearable robots have been proposed. In some cases, however, the cognitive function of these populations may have declined as well, and it may be insufficient to compensate only for their motor function deficiency. Perception-assist wearable robots, which can perceive environmental information using visual sensors attached to them, have been proposed to address this problem. This study addresses the problem of identifying motion intentions of the user of an upper-limb power-assist wearable robot, while the user engages in desired interactions with others. It is important to consider both the interacting parties in order to accurately predict proper interaction. Therefore, this paper presents an interaction recognition methodology by combining both the user’s motion intention and the other party’s motion intention with environmental information. A fuzzy reasoning model is proposed to semantically combine the motion intentions of both parties and environmental information. In this method, the motion intentions of both the user and the other party are simultaneously estimated using kinematic information and visual information, respectively, and they are employed for predicting the interactions between both parties. The effectiveness of the proposed approach is experimentally evaluated.

An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems

Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning activity under uncertainty. An experimental work was performed by exploiting three deductive knowledge bases produced with the aid of experts in the field. These were coded into models by employing the selected techniques and were subsequently elicited with data gathered from humans. The inferences produced by these models were in turn analysed according to common metrics of evaluation in the field of mental workload, in specific validity and sensitivity. Findings suggest that the variance of the inferences of expert systems and fuzzy reasoning models was higher, highlighting poor stability. Contrarily, that of argument-based models was lower, showing a superior stability of its inferences across knowledge bases and under different system configurations. The originality of this research lies in the quantification of the impact of defeasible argumentation. It contributes to the field of logic and non-monotonic reasoning by situating defeasible argumentation among similar approaches of non-monotonic reasoning under uncertainty through a novel empirical comparison.

A Fuzzy Case-Based Reasoning Model for Software Requirements Specifications Quality Assessment

Different software Quality Assurance (SQA) audit techniques are applied in the literature to determine whether the required standards and procedures within the Software Requirements Specification (SRS) phase are adhered to. The inspection of the Software Requirements Specification (iSRS) system is an analytical assurance tool which is proposed to strengthen the ability to scrutinize how to optimally create high-quality SRSs. The iSRS utilizes a Case-Based Reasoning (CBR) model in carrying out the SRS quality analysis based on the experience of the previously analyzed cases. This paper presents the contribution of integrating fuzzy Logic technique in the CBR steps to form a Fuzzy Case-Based Reasoning (FCBR) model for improving the reasoning and accuracy of the iSRS system. Additionally, for efficient cases retrieval in the CBR, relevant cases selection and nearest cases selection heuristic search algorithms are used in the system. Basically, the input to the relevant cases algorithm is the available cases in the system case base and the output is the relevant cases. The input to the nearest cases algorithm is the relevant cases and the output is the nearest cases. The fuzzy Logic technique works on the selected nearest cases and it utilizes similarity measurement methods to classify the cases into no-match, partial-match and complete-match cases. The features matching results assist the revised step of the CBR to generate a new solution. The implementation of the new FCBR model shows that converting numerical representation to qualitative terms simplifies the matching process and improves the decision-making of the system.

Application of Fuzzy Reasoning Model to the Estimation of Passengers of Railway Stations

Application of Fuzzy Reasoning Model to the Estimation of Passengers of Railway Stations

A Fuzzy Reasoning Model for Cervical Intraepithelial Neoplasia Classification Using Temporal Grayscale Change and Textures of Cervical Images During Acetic Acid Tests

Objective: A novel fuzzy reasoning model using temporal grayscale change and texture information of acetic acid test cervical images was developed to classify the patients at risk for Cervical Intraepithelial Neoplasia (CIN). Methods: Pre- and post-acetic acid test images were obtained from 505 patients (383 CIN negative and 122 CIN positive). An automatic image segmentation algorithm was implemented to extract the acetowhite region, followed by feature extraction of the: 1) temporal grayscale change from pre- to post-images; 2) texture coarseness; and 3) texture complexity of post-test images. The index of CIN was derived based on fuzzy reasoning incorporating expert knowledge. Logistic regression was built with cross validation to compare the classification performance between different combinations of features. Results: The averaged sensitivity was 80.8%, 80.9%, and 82.8% and specificity was 82.0%, 87.4%, and 86.2% for three individual features, respectively. The combination of all three features significantly improved the sensitivity (84.7%) without a significant loss of specificity (86.5%). The fuzzy reasoning model further improved the sensitivity significantly (sensitivity 85.9% and specificity 86.6%). Conclusion: Image texture features outperformed temporal grayscale change in specificity. Combining grayscale- and texture-based features could improve the overall classification performance, creating a synergistic effect. The proposed fuzzy reasoning model could further increase the sensitivity without requiring additional features. Significance: The proposed automatic CIN classification algorithm could provide a useful screening tool in the prevention of cervical cancers.

Risk Analysis of Emergency Based on Fuzzy Evidential Reasoning

Risk analysis of emergency is vital to effective emergency management. However, conventional analysis is challenged by the emerging problems as risk of emergency appearing increasingly complicated. The risk attributes of emergency originate in complicated sources, and their information is always incomplete. To ensure the efficiency and stability of emergency risk analysis, we proposed an elaborative approach composed of structural description framework and fuzzy evidential reasoning. Firstly, the risk attributes are identified by structural description framework. The information as evidence is obtained and normalized for further analysis. Secondly, risk analysis model with fuzzy evidential reasoning is constructed, and risk grade is evaluated. Finally, a certain railway project accident is taken as an example to test the model and some managerial insights are demonstrated. An approach combining structural description framework and fuzzy evidential reasoning model is feasible and effective; furthermore, it provides stable support for emergency risk analysis.

"Fuzzy Qualitative Reasoning Model for Astrocytoma Brain Tumor Grade Diagnosis"

Background: Magnetic Resonance Imaging (MRI) is the most prominently used image acquisition method for brain tumor diagnosis, treatment and research. Objective: In this paper, a fuzzy qualitative reasoning model for diagnosing the grade of Astrocytoma brain tumor using various subtypes of MR images (T1, T1c+, T2, Flair) is explained with its implementation details. Methods: The fuzzy model is implemented in 5 stages namely preprocessing, segmentation, feature extraction, feature selection and building a Fuzzy Inference System (FIS) for diagnosis. In preprocessing, anisotropic filtering is used to remove noise and artifacts whereas the edge information and smoothness are retained. Then the tumor region is segmented by applying active contour method. From the segmented tumor region, textural and shape features are extracted and stored along with the clinical parameters like age, gender and mass effect of the patient for feature selection. The features are analyzed in different dimensions like image, patient, patient with subtype, to determine the sensitive feature subset and its range that discriminates the grade of the tumor. Based on this outcome a Mamdani based fuzzy qualitative reasoning model is built with optimal rule set for tumor grade diagnosis. Findings: The constructed fuzzy model is validated using real data set of MR images and clinical report of patients. The grade of tumor identified is same as that specified in the patient\'s report and hence the model provides better accuracy. Novelty: The novelty of this research work are: subtypes of MR images with analysis in different dimensions, identification of optimal rule set (minimum number of rules without ambiguity), recognition of irregular shape tumor, suitable model for any knowledge based diagnosis. Keywords: Active Contour Method, Anisotropic Filtering, Astrocytoma Brain Tumor, Fuzzy Qualitative Reasoning Model, Magnetic Resonance Images, Optimal Rule Set, Textural and Shape Features

Fuzzy reasoning model to improve face illumination invariance

Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the automatic face recognition systems. This work tackles illumination variation challenge by proposing a new face image enhancement approach based on Fuzzy theory. The proposed Fuzzy reasoning model generates an adaptive enhancement which corrects and improves non-uniform illumination and low contrasts. The FRM approach has been assessed using four blind-reference image quality metrics supported by visual assessment. A comparison to six state-of-the-art methods has also been provided. Experiments are performed on four public data sets, namely Extended Yale-B, Mobio, FERET and Carnegie Mellon University Pose, Illumination, and Expression, showing very interesting results achieved by our approach.

Fuzzy MCDM-based GIS model for subsea oil pipeline route optimization: An integrated approach

ABSTRACTProper pipeline route selection is an integral component of a typical oil exploration and transportation project. Improperly selected routes could have severe consequences including pipe failures, oil spillage, and environmental disasters. Consequently, technologies like geographic information systems (GIS) are increasingly being used to facilitate the oil pipeline route selection procedure—especially for onshore routing projects. Surprisingly, not much has been documented on the application of GIS to offshore pipeline routing. With recent discoveries on the merits of offshore oil exploration, it is necessary to extend the analytical capabilities of GIS to the unique offshore domain. However, concerns have been raised regarding the limitations of GIS in accurately prioritizing diverse selection criteria in typical multi-criteria decision-making (MCDM) problems like route selection. Consequently, this paper addresses the offshore/subsea pipeline routing constraint using a hybrid decision support system (DSS), which integrates a GIS and fuzzy logic-based approximate reasoning (AR) models for optimal performance. The resultant spatial decision support system (SDSS) was successfully applied to a case study in Malaysia. The AR algorithm calculated the significance level of the multiple criteria using various fuzzy linguistic variables and membership functions. The aggregated priority ranking from different pipeline routing experts showed that the overall influence of the environmental criteria (61.4%) significantly exceeded that of other equally important criteria in the study area. These rankings were inputted into the SDSS to simulate various probable routes. Final results accurately highlighted an optimal route, which places a premium on the protection of environmental features in the subsea study area—in alignment with the preferences of majority of the experts.

Controlling Partnering Relations In Construction Operations Using Fuzzy Reasoning

AbstractThe article presents the use of the Mamdani fuzzy reasoning model to develop a proposal of a system controlling partnering relations in construction projects. The system input variables include: current assessments of particular partnering relation parameters, the weights of these parameters' impact on time, cost, quality and safety of implementation of construction projects, as well as the importance of these project assessment criteria for its manager. For each of the partnering relation parameters, the project’s manager will receive control recommendations. Moreover, the parameter to be improved first will be indicated. The article contains a calculation example of the system's operations.

A fuzzy multi-criteria decision support system for evaluating subsea oil pipeline routing criteria in East Malaysia

Selecting suitable pipeline routes is essential to the success of any oil exploration and transportation project, and proper prioritization of the diverse routing criteria is an integral component of the route selection endeavor. However, recent researches reveal that accurate prioritization of routing criteria, particularly in an offshore/subsea environment, has not been sufficiently addressed. With global attention rapidly expanding from terrestrial exploration to offshore and subsea oil pipeline operations, this study addresses the offshore pipeline routing ranking concerns. A hybrid multi-criteria decision support system was developed by integrating fuzzy logic-based approximate reasoning (AR) and Analytic Hierarchy Process (AHP) mathematical models. Twelve diverse criteria and sub-criteria in an oil-rich basin offshore east Malaysia were examined to determine their impact in the selection of an optimal pipeline route in the area. These were subsequently classified into three broad categories: economic, engineering, and environmental routing criteria. The integrated system (AR-AHP) was used to determine the relative importance of the criteria using different fuzzy membership functions and linguistic variables. Final results showed that the environmental criteria in the study area have the highest aggregated priority and influence (61.4 %) followed by the engineering criteria (27.6 %) and the economic criteria (11 %) datasets, respectively. This study thus establishes the fact that preservation of environmental factors is superior to the other strategic criteria in the marine study area.