Accurately estimating software effort is probably the biggest challenge facing software developers. Estimates done at the proposal stage has high degree of inaccuracy, where requirements for the scope are not defined to the lowest details, but as the project progresses and requirements are elaborated, accuracy and confidence on estimate increases. It is important to choose the right software ef...

Accurate and real-time forecasting of the price oil plays an important role in world economy. Research interest this type time series has increased considerably recent decades, since, due to characteristics series, it was a complicated task with inaccurate results. Concretely, deep learning models such as Convolutional Neural Networks (CNNs) Recurrent (RNNs) have appeared field promising result...

Neural network (NN) based modeling often requires trying multiple networks with different architectures and training parameters in order to achieve an acceptable model accuracy. Typically, only one of the trained networks is selected as "best" and the rest are discarded. The authors propose using optimal linear combinations (OLC's) of the corresponding outputs on a set of NN's as an alternative...

Due to its extensive use for motion control systems in industry, tuning of the proportional-integral-derivative (PID) controller parameters has been the focus of intensive research. In this paper, a novel tuning method for the parameters of PID controller based speed control of DC motor using genetic algorithm (GA) is proposed. The main advantage of the proposed method is that the mathematical ...

Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squ...

Stock price forecasting models with lower Mean Squared Error (MSE) are more “welcome" than those who have higher MSE. However, this “welcome" may disappear when lower MSE leads to an unexpected loss whereas higher MSE leads to a positive profit in a practical investment. The reason is that MSE aims to evaluate models in the view of “accuracy", which has nothing to do with profit. We therefore p...

Amikacin pharmacokinetic data in Kuwaiti (Arab) intensive care unit (ICU) patients are lacking. Fairly sparse serum amikacin peak and trough concentrations data were obtained from adult Kuwaiti ICU patients. The data were analysed using a nonparametric adaptive grid (NPAG) maximum likelihood algorithm. The estimations of the developed model were assessed using mean error (ME) as a measure of bi...

Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are proposed. Under simple random sampling without replacement (SRSWOR) scheme, the expressions of bias and mean-squared error (MSE) up to the first order of approximation are derived. The results obtained have been...

In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process. It is a striking observation that the ensemble mean often out-performs at least half of the ensemble members in mean squared error (measured with respect to observations). In fact, as demonstrated in the most recent IPCC report, the ensemble mean often out-performs all...