Use of new techniques to evaluate irrigation areas can enhance water use efficiency in agriculture. In this study, the fuzzy analytic hierarchy Process (FAHP) was used to qualitative land suitability for sprinkler irrigation and was compared with the parametric method. Evaluation based on parametric method showed that an area of about 1597.83 hectares (31%) of the lands was highly suitable (S1)...

Recent work in feature-based classi cation has focused on non-parametric techniques that can classify instances even when the underlying feature distributions are unknown. The inference algorithms for training these techniques, however, are designed to maximize the accuracy of the classi er, with all errors weighted equally. In many applications, certain errors are far more costly than others, ...

Modeling and solving real world problems is one of the most important issues in optimization problems. In this paper, we present an approach to solve Fuzzy Interval Flexible Linear Programming (FIFLP) problems that simultaneously have the interval ambiguity in the matrix of coefficients .In the first step, using the interval problem solving techniques; we transform the fuzzy interval flexible p...

This paper investigates patterns in the demand for barge transportation along the inland waterway system. Non-parametric techniques including both rolling regression and locally weighted regressions are used to visually analyze the pattern of elasticity estimates along the river at the pool level. The results of these non-parametric approaches visually indicate that barge demand elasticity may ...

Utilizing parametric and nonparametric techniques, we assess the role of a heretofore relatively unexplored ‘input’ in the educational process, homework, on academic achievement. Our results indicate that homework is an important determinant of student test scores. Relative to more standard spending related measures, extra homework has a larger and more significant impact on test scores. Howeve...

In a parametric optimization problem the genes code the real parameters of the fitness function. There are two coding techniques known under the names of: binary coded genes and real coded genes. The comparison between these two is a controversial subject since the first papers about parametric optimization have appeared. An objective analysis regarding the advantages and disadvantages of the t...

Markov chain Monte Carlo (MCMC) techniques represent an extremely flexible and powerful approach to Bayesian modeling. This work illustrates the application of such techniques to time-dependent reliability of components with repair. The WinBUGS package is used to illustrate, via examples, how Bayesian techniques can be used for parametric statistical modeling of time-dependent component reliabi...

We describe the notion of "equivalent kernels" and suggest that this provides a framework for comparing different classes of regression models, including neural networks and both parametric and non-parametric statistical techniques. Unfortunately, standard techniques break down when faced with models, such as neural networks, in which there is more than one "layer" of adjustable parameters. We ...

Bioinformatics is a data rich field which provides unique opportunities to use computational techniques, to understand and to organize information associated with Bio molecules such as DNA, RNA, and Proteins. DNA sequences can be converted into RNA and then proteomic sequences using transcription and translation. First these sequences are to be translated into a “gap sequence” consisting of int...

We propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametri...