permeability prediction problem has been examined using several methods such as empirical formulas, regression analysis and intelligent systems especially neural networks and fuzzy logic. this study proposes an improved and novel model for predicting permeability from conventional well log data. the methodology is integration of empirical formulas, multiple regression and neuro-fuzzy in a commi...

permeability can be directly measured using cores taken from the reservoir in the laboratory. due to high cost associated with coring, cores are available in a limited number of wells in a field. many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. the main obj...

Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. Early software estimation models are based on regression analysis or mathematical ...

In this paper we introduce regression-based pre-and postdiction procedures for PMON, a nonmonotonic logic for action and change with explicit time. We also provide an in depth analysis of problems with regression that occur when nondeterministic actions are introduced. We do this by employing Dijkstra's weakest liberal precondition operator, wlp. The presented work is related to recent work by ...

Experimental studies have shown that hatching rate depends, among other factors, on the main physical characteristics of the eggs. The physical parameters used in our work were egg weight, eggshell thickness, egg sphericity, and yolk per albumen ratio. The relationships of these parameters in the incubation process were modeled by Fuzzy logic. The rules of the Fuzzy modeling were based on the a...

We develop kernels for measuring the similarity between relational instances using background knowledge expressed in first-order logic. The method allows us to bridge the gap between traditional inductive logic programming (ILP) representations and statistical approaches to supervised learning. Logic programs are first used to generate proofs of given visitor programs that use predicates declar...

Software testing is typically an ad-hoc process where human testers manually write test inputs and descriptions of expected test results, perhaps automating their execution in a regression suite. This process is cumbersome and costly. This paper reports results on a framework to further automate this process. The framework consists of combining automated test case generation based on systematic...

A logic network to produce the sequency ordered Hadamard matrix H based on the property of gray code and orthogonal group codes is developed. The network uses a counter to generate Rademacher function such that the output of H will be in sequency. A general purpose shift register with output logic is used to establish a sequence of period P corresponding to a given value of order m of the Hadam...

Abstract The Tsetlin Machine is a recent supervised learning algorithm that has obtained competitive accuracy- and resource usage results across several benchmarks. It been used for convolution, classification, regression, producing interpretable rules in propositional logic. In this paper, we introduce the first framework reinforcement based on Machine. Our integrates value iteration with regr...