Laparoscopy Pneumoperitoneum Fuzzy Modeling

Abstract: Gas volume to intra-peritoneal pressure fuzzy modeling for evaluating pneumoperitoneum in videolaparoscopic surgery is proposed in this paper. The proposed approach innovates in using fuzzy logic and fuzzy set theory for evaluating the accuracy of the prognosis value in order to minimize or avoid iatrogenic injuries due to the blind needle puncture. In so doing, it demonstrates the feasibility of fuzzy analysis to contribute to medicine and health care. Fuzzy systems is employed here in synergy with artificial neural network based on backpropaga tion, multilayer perceptron architecture for building up numerical functions. Experimental data employed for analysis were collected in the accomplishment of the pneumoperitoneum in a random population of patients submitted to videolaparoscopic surgeries. Numerical results indicate that the proposed fuzzy mapping for describing the relation from the intra peritoneal pressure measures as function injected gas volumes succeeded in determinining a fuzzy model for this nonlinear system when compared to the statistical model

Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach

This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in convectional DEA is that inputs and outputs data are measured with precision. However, production agriculture takes place in an uncertain environment and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data is known to lie within specified intervals and empirically illustrate this approach using a group of 34 dairy producers in Pennsylvania. Compared to the convectional DEA scores that are point estimates, the computed fuzzy efficiency scores allow the decision maker to trace the performance of a decision-making unit at different possibility levels.fuzzy set theory, Data Envelopment Analysis, membership function, α-cut level, technical efficiency, Farm Management, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty, D24, Q12, C02, C44, C61,

COMPARISON OF CLASSICAL ANALYTIC HIERARCHY PROCESS (AHP) APPROACH AND FUZZY AHP APPROACH IN MULTIPLE-CRITERIA DECISION MAKING FOR COMMERCIAL VEHICLE INFORMATION SYSTEMS AND NETWORKS (CVISN) PROJECT

Radio Frequency Identification (RFID) has emerged as an important technology with many possible applications in a wide variety of fields. It is said that RFID can perform well in transportation system. Nebraska Department of Motor Vehicles (NEDMV) is using this technique to perform an analysis on utilizing RFID license plates to assist with Commercial Vehicle Information Systems and Networks (CVISN) program with the cooperation of many other stakeholders. Previous House of Quality (HOQ) analysis evaluates stakeholders’ needs and provides the pairwise comparison values of six important technical requirements for each stakeholder. Based on these, this research aims to seek for the comprehensive ranking of the six technical requirements. The weights of different technical requirements vary a lot according to different stakeholders. As a result, assumptions are made to make it possible that fuzzy analytic hierarchy process (AHP) approach could be used to give weight rankings of this multiple-criteria decision making problem. Problem comes out naturally that whether or not fuzzy AHP is appropriate to solve this problem. To verify the feasibility of application of fuzzy AHP to CVISN project problem, benchmarking comparison of classical AHP and fuzzy AHP approaches is performed. The comparison bases on a series of statistical models with 240 randomly generated statistical data. Results of comparison indicate that the pairwise weight values of AHP approach positively affect the difference between the two approaches, and fuzzy AHP could narrow the differences of weights among different criteria. Benchmarking models provide basic parameters, based on which prediction intervals are built to verify the outcomes of CVISN project given by fuzzy AHP. Results show that fuzzy AHP is an appropriate approach for CVISN project. Finally a comprehensive weight vector of six technical requirements is provided by fuzzy AHP, catering to the requirements of further research on choosing a best RFID system

An Empirical Validation of Object-Oriented Design Metrics for Fault Prediction

Object-oriented design has become a dominant method in software industry and many design metrics of object-oriented programs have been proposed for quality prediction, but there is no well-accepted statement on how significant those metrics are. In this study, empirical analysis is carried out to validate object-oriented design metrics for defects estimation. Approach: The Chidamber and Kemerer metrics suite is adopted to estimate the number of defects in the programs, which are extracted from a public NASA data set. The techniques involved are statistical analysis and neuro-fuzzy approach. Results: The results indicate that SLOC, WMC, CBO and RFC are reliable metrics for defect estimation. Overall, SLOC imposes most significant impact on the number of defects. Conclusions/Recommendations: The design metrics are closely related to the number of defects in OO classes, but we can not jump to a conclusion by using one analysis technique. We recommend using neuro-fuzzy approach together with statistical techniques to reveal the relationship between metrics and dependent variables, and the correlations among those metrics also have to be considered

Comparison of outlier detection at the edges of point clouds using statistical approach and fuzzy methodology: ground-based laser scanner field experiment and randomly simulated point cloud

The random error is following the features of normal distribution function (NDF) which those random errors deviated from the NDF's characteristics can be considered as outliers. In fact, the outliers exist inevitably in any observed parameter that is an undesirable part of the measurement's procedure due to its negative influence on the sensitivity analysis. It is therefore necessary to investigate more efficient methodologies especially for current remote sensing data processing and assimilations. In this paper, the comparisons of Baarda method as the conventional statistical methodology with the Fuzzy approach are presented to detect the outliers at the edges of two data groups: 1. The point cloud of ground-based laser scanner field experiment from one side of a wall, and 2. A group of randomly simulated distributed 3D point cloud. The results show that the Baarda method eliminates the outliers as soon as they are being found while the Fuzzy approach works critically based on the outputs of the statistical tests. Thus, the Fuzzy approach deals mostly with the residuals and those observed errors in the adjustment computational procedures. The obtained results about the successfulness rate of outlier detection for each method are separately presented in both graphical and statistical overview. Also, the capabilities of Fuzzy approach to detect the outliers in different point cloud's size and numbers of existing outliers at the edges of point cloud are investigated and discussed in details

Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method

Test of Fuzzy Logic Rules for Landslide Susceptibility Assessment

16 p.International audienceLandslide Susceptibility Assessment (LSA) is defined as the spatial probability for a landslide to be generated in an area for many environmental factors. Currently, two approaches are used: (i) the qualitative approach based on expert opinion and knowledge of the relationship between the observed phenomenon and some predisposing factors and (ii) the statistical approach based on the statistical analysis of the relationship between the observed landslide and some predisposing factors. This paper proposes an exploratory attempt to use Fuzzy Logic Rules for mapping landslide susceptibility. The technique allows to describe the role of each predisposing factor (predictive variable) and their optimal combination. The best predictive variables identified by Fuzzy Logic are then introduced in a statistical bivariate model. The simulated maps obtained by both approaches are then compared and evaluated with an expert map, build with the prescribed rules of the French PPR (Plan de Prévention des Risques) methodology, and considered as a map of reference

Searching for the first Near-Earth Object family

We report on our search for genetically related asteroids amongst the near-Earth object (NEO) population - families of NEOs akin to the well known main belt asteroid families. We used the technique proposed by Fu et al. (2005) supplemented with a detailed analysis of the statistical significance of the detected clusters. Their significance was assessed by comparison to identical searches performed on 1,000 'fuzzy-real' NEO orbit distribution models that we developed for this purpose. The family-free 'fuzzy-real' NEO models maintain both the micro and macro distribution of 5 orbital elements (ignoring the mean anomaly). Three clusters were identified that contain four or more NEOs but none of them are statistically significant at \geq 3{\sigma}. The most statistically significant cluster at the \sim 2{\sigma} level contains 4 objects with H < 20 and all members have long observational arcs and concomitant good orbital elements. Despite the low statistical significance we performed several other tests on the cluster to determine if it is likely a genetic family. The tests included examining the cluster's taxonomy, size-frequency distribution, consistency with a family-forming event during tidal disruption in a close approach to Mars, and whether it is detectable in a proper element cluster search. None of these tests exclude the possibility that the cluster is a family but neither do they confirm the hypothesis. We conclude that we have not identified any NEO families.Comment: 36 pages, 3 tables, 9 figures, accepted for publicatio

PERNCANAAN PENJADWALAN KANTOR GEDUNG PT GRESIK JASATAMA DENGAN METODE FUZZY LOGIC APLICATION FOR SCHEDULING (FLASH)

CV. Mukti Jaya Abadi is a company in the General Contractor and Supplier, for these companies in the project schedule based on the analysis of the head of the project and project estimator, Result there is uncertainty about the project completion time. Therefore it takes the right approach to determine the time of the project, in this case the fuzzy method is able to provide a good completion to the things that are uncertain. Fuzzy Logic Application for Scheduling (FLASH) is an appropriate method to accommodate uncertainty. FLASH does not require statistical data but only qualitative observation. The resultsobtained showtheprojectcanis overcomerange119daysto221dayswiththe mostlikelytime of163daysto170daysdefuzzyficationvalue