in order to study the effect of temperature and photoperiod (pp) on development rate of different vegetative and reproductive stages of canola, an experiment was conducted at agricultural research station of gonbad during 2005-2007. the experiment was a randomized complete block design arranged in a split-plot in two conditions, for example supplemental irrigation and rainfed. there were differ...

Non-linear regression is a fundamental and yet under-developing methodology in solving many problems in Artificial Intelligence. The canonical control and predictions mostly utilize linear models or multi-linear models. However, due to the high non-linearity of the systems, those linear prediction models cannot fully cover the complexity of the problems. In this paper, we propose a robust two-s...

Question: How can mammalian community characteristics be used to estimate regional precipitation? Data: Global distribution data of large mammals and their ecomorphology; global climate data. Research methods: Non-linear regression-tree analysis and linear regression. Conclusions: The methods unravelled the complex relationships between the environment and the characteristics of mammalian commu...

Quantitative Structure-Property Relationship (QSPR) models for modeling and predicting thermodynamic properties such as the enthalpy of vaporization at standard condition (ΔH˚vap kJ mol-1) and normal temperature of boiling points (T˚bp K) of 57 mono and Polycyclic Aromatic Hydrocarbons (PAHs) have been investigated. The PAHs were randomly separated into 2 groups: training and test sets. A set o...

Accurate estimation of evaporation is important for design, planning and operation of water systems. In arid zones where water resources are scarce, the estimation of this loss becomes more interesting in the planning and management of irrigation practices. This paper investigates the ability of artificial neural networks (ANNs) technique to improve the accuracy of daily evaporation estimation....

Linear regression is arguably one of the most widely used statistical methods. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs) in the linear regression context. In general, the IM framework is able to produce meaningful probabilistic summaries of the sta...

Since it is generally recognised that models evaluated on the data that was used for constructing them are overly optimistic, in predictive modeling practice, the assessment of a model’s predictive performance frequently relies on a one-shot train-and-test split between observations used for estimating a model, and those used for validating it. Previous research has indicated the usefulness of ...

This paper describes the use of Active Shape Models to segment the tibia bone in ultrasound images. Using CT images from three volunteers, a training set consisting of 60 tibia contours, each represented by 45 labeled points, is first established. After aligning the contours in the training set, a Point Distribution Model (PDM) is generated that includes a mean shape of tibia and six modes of s...

The quality of individual ratings of conformation traits can commonly be evaluated by calculating inter-rater correlations and repeatability coefficients. We present an approach in which we associate the individual rating scores with the underlying horse shapes derived from standardized images, performing a shape regression. Therefore, we analyzed the shape of 102 Lipizzan stallions from the Sp...

A multivariate normal statistical model defined by the Markov properties determined by an acyclic digraph admits a recursive factorization of its likelihood function (LF) into the product of conditional LFs, each factor having the form of a classical multivariate linear regression model (≡ MANOVA model). Here these models are extended in a natural way to normal linear regression models whose LF...