The objective of this study was to develop prediction mathematical equations to compute reinforcement steel quantity in traditional residential buildings based on 158 sets of data collected in the West Bank in Palestine. The records related to the quantities were collected from consultancy firms that provide reinforced concrete design services. The data were collected for residential buildings ...

BACKGROUND Tuberculosis (TB) is a serious public health issue in developing countries. Early prediction of TB epidemic is very important for its control and intervention. We aimed to develop an appropriate model for predicting TB epidemics and analyze its seasonality in China. METHODS Data of monthly TB incidence cases from January 2005 to December 2011 were obtained from the Ministry of Heal...

In ubiquitous computing systems, applications must be able to respond to dynamic context changes in order to provide suitable services to users. A promising solution consists of developing context aware applications which automatically change their behavior according to the user needs, the user preferences, the available resources and the surrounding environment. Furthermore, a context aware ap...

Abstract: The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore,...

Percentage error (relative to the observed value) is often felt to be more meaningful than the absolute error in isolation. The mean absolute percentage error (MAPE) is widely used in forecasting as a basis of comparison, and regression models can be fitted which minimize this criterion. Unfortunately, no formula exists for the coefficients, and models for a given data set may not be unique. We...

This paper presents an improved software estimation model, which uses to estimate developing effort of e-Learning's contents. This model is called the e-Learning courseware Effort Estimate Model (EEEM). This model uses special designed storyboard to delineate the e-Learning courseware's contents and define the measurement metrics. Research was conducted using statistical data from the real worl...

This paper presents a practical method for short-term load forecast problem using artificial neural network (ANN) combined similar days approach. Neural networks applied in traditional prediction methods all use similar days data to learn the trend of similarity. However, learning all similar days data is a complex task, and does not suit the training of neural network. A Euclidean norm with we...

Similar to other ad hoc networks, VANETs need to change and adapt to their surrounding environment. Having various internal and external facilities to change network structure and parameters, VANETS are a good target for self-adaptive and self-managing systems. In order to achieve an autonomic system, selfmanagement should be implemented in the system architecture. To address the autonomic mana...

Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR). Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at ...

MOTIVATION Many pathway analysis (or gene set enrichment analysis) methods have been developed to identify enriched pathways under different biological states within a genomic study. As more and more microarray datasets accumulate, meta-analysis methods have also been developed to integrate information among multiple studies. Currently, most meta-analysis methods for combining genomic studies f...