Computational Investigation of Hand Foot Mouth Disease Dynamics with Fuzziness

The first major outbreak of the severely complicated hand,foot and mouth disease(HFMD),primarily caused by enterovirus 71,was reported in Taiwan in 1998.HFMD surveillance is needed to assess the spread of HFMD.The parameters we use in mathematical models are usually classical mathematical parameters,called crisp parameters,which are taken for granted.But any biological or physical phenomenon is best explained by uncertainty.To represent a realistic situation in any mathematical model,fuzzy parameters can be very useful.Many articles have been published on how to control and prevent HFMD from the perspective of public health and statistical modeling.However,few works use fuzzy theory in building models to simulateHFMDdynamics.In this context,we examined anHFMD model with fuzzy parameters.A Non Standard Finite Difference(NSFD)scheme is developed to solve the model.The developed technique retains essential properties such as positivity and dynamic consistency.Numerical simulations are presented to support the analytical results.The convergence and consistency of the proposed method are also discussed.The proposed method converges unconditionally while the many classical methods in the literature do not possess this property.In this regard,our proposed method can be considered as a reliable tool for studying the dynamics of HFMD.

Computational Analysis for Computer Network Model with Fuzziness

A susceptible,exposed,infectious,quarantined and recovered(SEIQR)model with fuzzy parameters is studied in this work.Fuzziness in the model arises due to the different degrees of susceptibility,exposure,infectivity,quarantine and recovery among the computers under consideration due to the different sizes,models,spare parts,the surrounding environments of these PCs and many other factors like the resistance capacity of the individual PC against the virus,etc.Each individual PC has a different degree of infectivity and resis-tance against infection.In this scenario,the fuzzy model has richer dynamics than its classical counterpart in epidemiology.The reproduction number of the developed model is studied and the equilibrium analysis is performed.Two different techniques are employed to solve the model numerically.Numerical simulations are performed and the obtained results are compared.Positivity and convergence are maintained by the suggested technique which are the main features of the epidemic models.

On Aesthetic Recognition of Literary Fuzziness and Translation

Due to the prevalence of fuzziness in literary works and in poetry in particular,the author puts forward the view that some skills such as rhetoric devices,abstraction,meaning manifestation,creation of imagination context are to be adopted in the translation of literary fuzziness from the perspective of aesthetic recognition to achieve an integration between the textual implication and the readers' expectation horizon.And the author takes the translation of Chinese classic poems for example,through detailed analysis and comparison,proves the feasibility of the viewpoint.

The Analysis of Previous Research on Fuzziness in Legal Texts

Different from precision,fuzziness owns some special features.This paper tries to do a preliminary analysis of previous research on fuzziness and fuzzy expressions in legal texts.

Fuzziness in the Teaching of College English Reading

English fuzziness has been studied from the point of view of linguistics. For my article is amateurish, rather than scholarly. In this paper Ⅰ shall first briefly discuss fuzziness in English language and its relation to English teaching and learning. Ⅰ shall then make use of this discussion to the analysis of applying fuzziness to the teaching of college English reading.

The Pragmatic Fuzziness of Diplomatic Language in the Light of Adaptation Theory

This paper,based on Adaptation Theory,explores the diplomatic language psychologically,socially and physically to achieve safe,precise,polite,and wise communication.

Finding a Link between Randomness and Fuzziness

If sample realizations are intervals, if the upper and the lower boundaries of such intervals are realizations of two independently distributed random variables, the two probability laws together lead to some interesting assertions. In this article, we shall attempt to remove certain confusions regarding the relationship between probability theory and fuzzy mathematics.

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

There Also Can Be Fuzziness in Quantum States Itself—Breaking through the Framework and the Principle of Quantum Mechanics

In this paper, an attempt is made to synthesize fuzzy mathematics and quantum mechanics. By using the method of fuzzy mathematics to blur the probability (wave) of quantum mechanics, the concept of fuzzy wave function is put forward to describe the fuzzy quantum probability. By applying the non-fuzzy formula of fuzzy quantity and Schrödinger wave equation of quantum mechanics, the membership function equation is established to describe the evolution of the fuzzy wave function. The concept of membership degree amplitude is introduced to calculate fuzzy probability amplitude. Some important concepts in fuzzy mathematics are also illustrated.

基于网格划分的常发性拥堵区域识别及演化模式分析

为准确识别常发性拥堵区域,分析其拥堵传播方向,疏通拥堵源头和阻断拥堵传播路径,提出一种基于网格模型和Fuzzy Core DBSCAN算法的常发性拥堵区域识别及演化模式分析方法。首先,对城市路网进行网格化处理,结合出租车GPS数据综合分析网格内车辆轨迹数量和轨迹加权平均速度,构建网格内部交通拥堵状态判别模型。然后,利用结合模糊集合论与DBSCAN算法扩展得到的Fuzzy Core DBSCAN算法识别常发性拥堵区域,选取拥堵规模、拥堵传播方向(即两个相邻网格之间的传播次数和传播概率)和关键网格3个指标来分析常发性拥堵的演化模式。最后,以黑龙江省哈尔滨市二环路范围内网格区域为例进行实证分析,结果表明:所识别出的常发性拥堵区域关键网格G(14,13)一周内拥堵传播到相邻网格共85次,其中27次(以31.8%的概率)沿西大直街向北传播至网格G(15,14);与百度地图开放平台数据相比,基于网格模型的拥堵判别结果准确率达85%;基于Fuzzy Core DBSCAN算法识别分析出的常发性拥堵区域拥堵演化模式与百度地图开放平台路况时变过程吻合。这表明融合Fuzzy Core DBSCAN算法与网格模型可有效识别常发性拥堵区域中关键网格的拥堵传播方向,能为制定科学合理的缓堵方案提供支撑。

旧工业建筑再生利用关键制约因素研究

科学识别旧工业建筑再生利用的关键制约因素,对促进旧工业建筑再生利用实施及城市可持续更新具有重要意义。首先,通过扎根理论总结11个影响西安市旧工业建筑再生利用的制约因素;然后,采用Fuzzy Decision-Making and Trial Evaluation Laboratory(DEMATEL)方法对制约因素之间的关系进行分析,进而确定影响旧工业建筑再生利用的关键制约因素。结果显示,相应的法律法规缺乏、支持政策不足、旧工业建筑信息不齐全和利益相关者认识不足是西安市旧工业建筑再生利用的关键制约因素。这表明政府在旧工业建筑再生利用发展中扮演至关重要的角色。因此,建议优先采取措施使政府对旧工业建筑再生利用加大重视力度并予以支持,发挥政府的引导作用,逐步推动旧工业建筑的再生利用。

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

EOQ model of deteriorating items for fuzzy demand and learning in fuzziness with finite horizon

This study investigates the effect of learning in fuzziness by considering fuzzy demand in theEOQ model for deteriorating items under a finite time horizon.The crisp equivalent form of the fuzzy objective function is obtained by employing the centroid method.Using calculus,the number of replenishments which optimizes the fuzzy objective function is derived.The model is extended by applying learning in fuzziness and an algorithm is developed to determine the number of replenishments.Numerical illustrations are provided for the model under a crisp,fuzzy and fuzzylearning environment.Numerical results reveal that the cost is lower with learning in fuzziness than that of without learning in fuzziness.Besides,results indicate that the learning in fuzziness is more effective whenever the parameter has higher impreciseness in the estimation of its value.

Complex Decision Modeling Framework with Fairly Operators and Quaternion Numbers under Intuitionistic Fuzzy Rough Context

The main goal of informal computing is to overcome the limitations of hypersensitivity to defects and uncertainty while maintaining a balance between high accuracy,accessibility,and cost-effectiveness.This paper investigates the potential applications of intuitionistic fuzzy sets(IFS)with rough sets in the context of sparse data.When it comes to capture uncertain information emanating fromboth upper and lower approximations,these intuitionistic fuzzy rough numbers(IFRNs)are superior to intuitionistic fuzzy sets and pythagorean fuzzy sets,respectively.We use rough sets in conjunction with IFSs to develop several fairly aggregation operators and analyze their underlying properties.We present numerous impartial laws that incorporate the idea of proportionate dispersion in order to ensure that the membership and non-membership activities of IFRNs are treated equally within these principles.These operations lead to the development of the intuitionistic fuzzy rough weighted fairly aggregation operator(IFRWFA)and intuitionistic fuzzy rough ordered weighted fairly aggregation operator(IFRFOWA).These operators successfully adjust to membership and non-membership categories with fairness and subtlety.We highlight the unique qualities of these suggested aggregation operators and investigate their use in the multiattribute decision-making field.We use the intuitionistic fuzzy rough environment’s architecture to create a novel strategy in situation involving several decision-makers and non-weighted data.Additionally,we developed a novel technique by combining the IFSs with quaternion numbers.We establish a unique connection between alternatives and qualities by using intuitionistic fuzzy quaternion numbers(IFQNs).With the help of this framework,we can simulate uncertainty in real-world situations and address a number of decision-making problems.Using the examples we have released,we offer a sophisticated and systematically constructed illustrative scenario that is intricately woven with the complexity ofmedical evaluation in order to thoroughly assess the relevance and efficacy of the suggested methodology.

Optimization Algorithms of PERT/CPM Network Diagrams in Linear Diophantine Fuzzy Environment

The idea of linear Diophantine fuzzy set(LDFS)theory with its control parameters is a strong model for machine learning and optimization under uncertainty.The activity times in the critical path method(CPM)representation procedures approach are initially static,but in the Project Evaluation and Review Technique(PERT)approach,they are probabilistic.This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy(LDF)environment.The LDF expected task time,LDF variance,LDF critical path,and LDF total expected time for determining the project network are all computed using LDF numbers as the time of each activity in the project network.The primary premise of the LDF-PERT approach is to address ambiguities in project network activity timesmore simply than other approaches such as conventional PERT,Fuzzy PERT,and so on.The LDF-PERT is an efficient approach to analyzing symmetries in fuzzy control systems to seek an optimal decision.We also present a new approach for locating LDF-CPM in a project network with uncertain and erroneous activity timings.When the available resources and activity times are imprecise and unpredictable,this strategy can help decision-makers make better judgments in a project.A comparison analysis of the proposed technique with the existing techniques has also been discussed.The suggested techniques are demonstrated with two suitable numerical examples.

Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

Rough set theory is an important tool to solve uncertain problems. Attribute reduction, as one of the core issues of rough set theory, has been proven to be an effective method for knowledge acquisition. Most of heuristic attribute reduction algorithms usually keep the positive region of a target set unchanged and ignore boundary region information. So, how to acquire knowledge from the boundary region of a target set in a multi-granulation space is an interesting issue. In this paper, a new concept, fuzziness of an approximation set of rough set is put forward firstly. Then the change rules of fuzziness in changing granularity spaces are analyzed. Finally, a new algorithm for attribute reduction based on the fuzziness of 0.5-approximation set is presented. Several experimental results show that the attribute reduction by the proposed method has relative better classification characteristics compared with various classification algorithms.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

A Fuzzy Trust Management Mechanism with Dynamic Behavior Monitoring for Wireless Sensor Networks

Traditional wireless sensor networks(WSNs)are typically deployed in remote and hostile environments for information collection.The wireless communication methods adopted by sensor nodes may make the network highly vulnerable to various attacks.Traditional encryption and authentication mechanisms cannot prevent attacks launched by internal malicious nodes.The trust-based security mechanism is usually adopted to solve this problem in WSNs.However,the behavioral evidence used for trust estimation presents some uncertainties due to the open wireless medium and the inexpensive sensor nodes.Moreover,how to efficiently collect behavioral evidences are rarely discussed.To address these issues,in this paper,we present a trust management mechanism based on fuzzy logic and a cloud model.First,a type-II fuzzy logic system is used to preprocess the behavioral evidences and alleviate uncertainty.Then,the cloud model is introduced to estimate the trust values for sensor nodes.Finally,a dynamic behavior monitoring protocol is proposed to provide a balance between energy conservation and safety assurance.Simulation results demonstrate that our trust management mechanism can effectively protect the network from internal malicious attacks while enhancing the energy efficiency of behavior monitoring.

Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy Decision-Making Approach

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in cybersecurity.KSA has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage.The widespread usage of Information Technology(IT)and IT Enable Services(ITES)reinforces securitymeasures.The constantly evolving cyber threats are a topic that is generating a lot of discussion.In this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region.The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online.Different competency models have been devised to address the necessary cybercrime competencies in this context.The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security.To address this research query,the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(F.AHP),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy.The similarities and differences between the three methods are also discussed.This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority.The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for SaudiArabia.Moreover,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.