A Quality Evaluation System for Dissertation Based on Fuzzy Analytic Hierarchy Process

A dissertation is a research report or scientific paper written by an author to obtain a certain degree. It reflects postgraduates’ research achievements and the educational quality of an institute, even a country. To construct an optimized quality evaluation system for postgraduate dissertation (QESPD), we summarized the influencing factors and invited 10 experienced specialists to rate and prioritize them based on fuzzy analytic hierarchy process. Four primary indicators (innovation, integrity, scientificity and normativity) and 16 sub-indicators were selected to form the evaluation system. The order of primary indicators by weight, was innovation (0.4269), scientificity (0.2807), integrity (0.1728) and normativity (0.1196). The top five sub-dimensions were theoretical originality, scientific value, data reliability, design rationality and evidence credibility. To demonstrate the effectiveness of the proposed system, a case study was performed. In the case study, it was demonstrated that the established two-index-hierarchy QESPD in this study was a more scientific and reasonable evaluation system worthy of promotion and application.

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom investigated in multiple factories.Furthermore,the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP.In the current study,DTHFSP with fuzzy processing time was investigated,and a novel Q-learning-based teaching-learning based optimization(QTLBO)was constructed to minimize makespan.Several teachers were recruited for this study.The teacher phase,learner phase,teacher’s self-learning phase,and learner’s self-learning phase were designed.The Q-learning algorithm was implemented by 9 states,4 actions defined as combinations of the above phases,a reward,and an adaptive action selection,which were applied to dynamically adjust the algorithm structure.A number of experiments were conducted.The computational results demonstrate that the new strategies of QTLBO are effective;furthermore,it presents promising results on the considered DTHFSP.

Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and uncertain.Traditional data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw data.Data fusion is the process of integrating multiple data sources.Data filtering means examining a dataset to exclude,rearrange,or apportion data according to the criteria.Different sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional methods.The advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional methods.However,many open issues still exist as machine learning algorithms are used for data fusion.From the literature,nine issues have been identified irrespective of any application.The decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these issues.It helps to get the weights for each corresponding issue and rank issues based on these calculated weights.The most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted 0.141.The least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

Portfolio selection: a fuzzy-ANP approach

This study developed specific criteria and a fuzzy analytic network process(FANP)to assess and select portfolios on the Tehran Stock Exchange(TSE).Although the portfolio selection problem has been widely investigated,most studies have focused on income and risk as the main decision-making criteria.However,there are many other important criteria that have been neglected.To fill this gap,first,a literature review was conducted to determine the main criteria for portfolio selection,and a Likert-type questionnaire was then used to finalize a list of criteria.Second,the finalized criteria were applied in an FANP to rank 10 different TSE portfolios.The results indicated that profitability,growth,market,and risk are the most important criteria for portfolio selection.Additionally,portfolios 6,7,2,4,8,1,5,3,9,and 10(A6,A7,A2,A4,A8,A1,A5,A3,A9,and A10)were found to be the best choices.Implications and directions for future research are discussed.

Evaluating the conceptual design schemes of complex products based on FAHP using fuzzy number

In the conceptual design stage of complex products, CBR（Case-Based Reasoning） tool is useful to offer a feasible set of schemes. Then the most adaptive scheme can be generated through a procedure of comparison and evaluation. The procedure is essentially a multiple criteria decision-making problem. The traditional multiple criteria programming is not flexible enough in executing the system evaluation algorithm due to both the limited experimental data and the lack of human experiences. To make the CBR tool to be more efficient, a new method for the best choice among the feasible schemes based on the Fuzzy AHP using Fuzzy numbers （FFAHP） is proposed. Since the final results become a problem of ranking the mean of fuzzy numbers by the optimism of decision-maker using the FFAHP, its execution is much more intuitive and effective than with the traditional method.

Comparison of Risk Assessment for a Nuclear Power Plant Construction Project Based on Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process

Recently, plant construction throughout the world, including nuclear power plant construction, has grown significantly. The scale of Korea’s nuclear power plant construction in particular, has increased gradually since it won a contract for a nuclear power plant construction project in the United Arab Emirates in 2009. However, time and monetary resources have been lost in some nuclear power plant construction sites due to lack of risk management ability. The need to prevent losses at nuclear power plant construction sites has become more urgent because it demands professional skills and large-scale resources. Therefore, in this study, the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) were applied in order to make comparisons between decision-making methods, to assess the potential risks at nuclear power plant construction sites. To suggest the appropriate choice between two decision-making methods, a survey was carried out. From the results, the importance and the priority of 24 risk factors, classified by process, cost, safety, and quality, were analyzed. The FAHP was identified as a suitable method for risk assessment of nuclear power plant construction, compared with risk assessment using the AHP. These risk factors will be able to serve as baseline data for risk management in nuclear power plant construction projects.

Risk Assessment M ode of Sale-Leaseback——Based on Fuzzy Analytic Hierarchy Process

Sale-leaseback is paid special focus on for its great application in China in recent years.By giving a detailed analysis,an analysis of the features of sale-leaseback and a study of the risks of it are given in this paper.Then a quantitative analysis of commercial real estate sale-leaseback is carried out.The scientific theoretical analysis is provided in this paper for risk assessment in commercial real estate sale-leaseback mode in China.An effective approach is put forward for sale-leaseback project evaluation and prevention of risk.A useful means is given to supervise commercial real estate sale-leaseback mode for relevant departments and a certain contribution is made to a further sound development of commercial real estate sale-leaseback mode.

Component-Based Software Trustworthiness Fuzzy Analytic Hierarchy Process Model

For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses, the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components, dividing the system into several layers from target layer to scheme layer, evaluating the scheme advantages disadvantages by group decision-making, the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.

Multiyear Discrete Stochastic Programming with a Fuzzy Semi-Markov Process

Drought conditions at a given location evolve randomly through time and are typically characterized by severity and duration. Researchers interested in modeling the economic effects of drought on agriculture or other water users often capture the stochastic nature of drought and its conditions via multiyear, stochastic economic models. Three major sources of uncertainty in application of a multiyear discrete stochastic model to evaluate user preparedness and response to drought are: (1) the assumption of independence of yearly weather conditions, (2) linguistic vagueness in the definition of drought itself, and (3) the duration of drought. One means of addressing these uncertainties is to re-cast drought as a stochastic, multiyear process using a “fuzzy” semi-Markov process. In this paper, we review “crisp” versus “fuzzy” representations of drought and show how fuzzy semi-Markov processes can aid researchers in developing more robust multiyear, discrete stochastic models.

Transformation and risk assessment of the East Kolkata Wetlands (India)using fuzzy MCDM method and geospatial technology

The East Kolkata Wetlands (EKW) is located on the eastern periphery of the city of Kolkata and extends up to theBidyadhari-Matla River confluence. It is a Ramsar Site and acts as an absorber basin for a large number of con-taminants drained from Kolkata. Agricultural lands, sewage-fed fisheries, garbage dumping fields, horticulture,and built-up areas are included in this protected area, that covers approximately 125 km2. It reveals that climatechange reduces the variety of wetland ecosystem services and increases socio-economic vulnerability and eco-nomic stress. The human encroachment, reclamation of land for agriculture, aquaculture, and urban expansion inand around Kolkata has recently adversely threatened the EKW. The remotely sensed data, socio-economic data,and responses of inhabitants have been used to analyse the EKW’s risk and vulnerability. We employed geospatialanalysis by using the Multi-Criteria Decision Making (MCDM) method using nine risk factors. An in-depth analysisof the EKW using geospatial techniques and the Fuzzy Analytic Hierarchy Process (FAHP) helped to understandthe EKW transformations through vulnerability and risk analysis. The results show that the transformation of thewetland to aquaculture, eutrophication and pollution, road proximity, waste dumping, population density, andgrowth are the main factors for the deteriorating health, quality, and environment of the EKW. It also reveals thatquantitative and qualitative evaluations of ecosystem services, wetland degradation, transformation, and cause-effect rapport should be periodically assessed using scientific methods like FAHP, RS, GIS to formulate resilient,integrated plans and strategy for the sustainable management of the EKW.

Analysis of structural fuzzy random seismic response

The earthquake loads specified for the aseismic structure design depend mainly on the predicted seismic intensity and the site soil classification.Earthquake excitation not only has evident randomness but also has strong fuzziness owing to the imprecision in the definition and evaluation norms of seismic intensity and site soil classification.A realistic analysis and design of structural systems subjected to such earthquake excitations must account for the uncertainty arising from both randomness and fuzziness simultaneously in a consistent and rational manner.In this paper,the models of stationary and nonstationary filtered white noise fuzzy stochastic processes of the earthquake ground motion are set up.And then the analysis methods for fuzzy random seismic response of single degree of freedom and multi degree of freedom systems are put forward by the theory of fuzzy stochastic dynamical systems(Zhang,1991;Zhang and Wang,1993).

Research on the fuzzy relationship between the precursory anomalous elements and earthquake elements

This paper deals with the fuzzy information processing method in the research of the relationship between two variables or among multi-variables, with the undetermined or fuzzy features, different from that in the traditional statistical method. The reliability and effectiveness of the method have been tested and confirmed in the numerical simulation for a set of man-made data of precursory anomalous parameters and earthquake elements. Finally the relation between the actual monthly frequency of small earthquakes occurring in Huoshan, Anhui Province, China and the magnitude of future stronger earthquakes, as two variables, has been analyzed by the method. It seems to the authors that more reasonable and perfect results could be given with a quantitative analysis of accession degree in fuzzy mathematics by using this method than that of traditional statistical correlation analysis.

Fuzzy Stochastic Differential Equations Driven by a Fuzzy Brownian Motion

In our previous work, we study fuzzy Itôintegrals driven by a fuzzy Brownian motion. In this article, we continue this study. The purpose of this paper is to study the weak uniqueness of fuzzy stochastic differential equations taking into account fuzzy Brownian motion. For instance, we construct the fuzzy stochastic differential equation driven by a fuzzy Brownian motion. To define and prove our results, we use the fuzzification, the alpha cut method and the Hausdorff distance between two fuzzy quantities. Some results are to our credit in this article like the instance, we construct the fuzzy stochastic differrential equation driven by fuzzy Brownian motion. Furthermore, we develop fuzzy Itôcalculus driven by a fuzzy Brownian motion. Our result complement existing ones in that the fuzzy version of Brownian motion is taken into account.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.

Studies on Multi-Criteria Decision-Making-Based Healthcare Systems Using the Machine Learning

There is a lot of information in healthcare and medical records.However,it is challenging for humans to turn data into information and spot hidden patterns in today’s digitally based culture.Effective decision support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments and in different healthcare management activities.This paper presented an extensive literature survey for healthcare systems using machine learning based on multi-criteria decision-making.Various existing studies are considered for review,and a critical analysis is being done through the reviews study,which can help the researchers to explore other research areas to cater for the need of the field.

A Comprehensive Model for Evaluating Coalbed Methane Reservoirs in China

Coalbed methane reservoir （CBMR） evaluation is important for choosing the prospective target area for coalbed methane exploration and production. This study aims at identifying the characteristic parameters and methods to evaluate CBMR. Based on the geological surveys, laboratory measurements and field works, a four-level analytic hierarchy process （AHP） model for CBMR evaluation is proposed. In this model, different weights are prioritized and assigned on the basis of three main criteria （including reservoir physical property, storage capacity and geological characteristics）, 15 sub-criteria, and 18 technical alternatives; the later of which are discussed in detail. The model was applied to evaluate the CBMR of the Permo-Carboniferous coals in the Qinshui Basin, North China. This GIS-based fuzzy AHP comprehensive model can be used for the evaluation of CBMR of medium-high rank （mean maximum vitrinite reflectance 〉0.5 %） coal districts in China.

Innovative Product Design Based on Customer Requirement Weight Calculation Model

In the processes of product innovation and design, it is important for the designers to find and capture customer＇s focus through customer requirement weight calculation and ranking. Based on the fuzzy set theory and Euclidean space distance, this paper puts forward a method for customer requirement weight calculation called Euclidean space distances weighting ranking method. This method is used in the fuzzy analytic hierarchy process that satisfies the additive consistent fuzzy matrix. A model for the weight calculation steps is constructed; meanwhile, a product innovation design module on the basis of the customer requirement weight calculation model is developed. Finally, combined with the instance of titanium sponge production, the customer requirement weight calculation model is validated. By the innovation design module, the structure of the titanium sponge reactor has been improved and made innovative.

Analysis of Critical Factors in Manufacturing by Adopting a Cloud Computing Service

The advantages of a cloud computing service are cost advantages,availability,scalability,flexibility,reduced time to market,and dynamic access to computing resources.Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors.Tofind critical factors,this studyfirst reviewed the literature and established a three-layer hierarch-ical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework.Then,a hybrid method that combines two multi-criteria decision-making tools—called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage—was used to objectively identify critical factors for the adop-tion of a cloud computing service,replacing the subjective decision of the authors.The results of this study determinedfive critical factors,namely data access secur-ity,information transmission security,senior management support,fallback cloud management,and employee acceptance.Finally,the paper presents thefindings and implications of the study.

Study on the Theory and Method of Real Estate Property Right Appraisal

This paper first introduces the overview of real estate appraisal theory,the basic situation of real estate appraisal,the basic principles and characteristics of real estate appraisal,and three main appraisal methods.On this basis,the specific use scope of each method,the meaning of each parameter,and the influencing factors are analyzed.Then,based on the in-depth study of various existing appraisal theories and methods,a new appraisal method is proposed:the appraisal analysis method based on fuzzy analytic hierarchy process.

Practical Issues Concerning Moment Invariants

In this report several practical issues about moment invariants with application to image classification are concerned. A modified formulation for the approximation of the moments of digital images is suggested. Four computational procedures and their corresponding noise performances are studied in detail.