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...

The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model ...

BACKGROUND This study aimed to evaluate the prediction accuracy of postoperative refractions using partial coherence interferometry (IOL-Master) and applanation ultrasound (AL-3000) assisted with corneal topography (TMS-4) in eyes that had undergone myopic laser-assisted in situ keratomileusis (LASIK). METHODS Haigis-L formula, Koch-Maloney method using Haigis formula, Shammas clinically deri...

To simulate the broiler growth the input variables were: day of year, vents opening, wind velocity, external temperature and absolute humidity, the maximum, average and minimum of the internal temperature and absolute humidity. For that purpose, two techniques were applied, a multi-layer perceptron (MLP) static Neural Network (NN) and the Layered Digital Dynamic Network (LDDN) which were applie...

Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed ap...

The present approach to the MAE-based design of stack filters for image restoration does not always produce the desired visual result. Thus, in this paper, a new stack filter design algorithm is developed. It is based upon a Weighted Mean Absolute Error (WMAE) criterion instead of the traditional MAE criterion, which assigns the same weights to all errors. The weights in this WMAE criterion are...

BACKGROUND Generalized linear models (GLMs) have recently been introduced into cost data analysis. GLMs, transformations of the linear regression model, are characterized by a particular response distribution from one of the exponential family of distributions and monotonic link function which relates the response mean to a scale on which additive model effects operate. OBJECTIVES This study ...

We evaluate and compare two common methods, artificial neural networks (ANN) and support vector regression (SVR), for predicting energy productions from a solar photovoltaic (PV) system in Florida 15 min, 1 h and 24 h ahead of time. A hierarchical approach is proposed based on the machine learning algorithms tested. The production data used in this work corresponds to 15 min averaged power meas...

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...

In this study we model the daily rainfall occurrence using Markov Chain Analogue Yearmodel (MCAYM) and the intensity or amount of daily rainfall using three different probability distributions; gamma, exponential and mixed exponential distributions. Combining the occurrence and intensity model we obtain Markov Chain Analogue Year gamma model (MCAYGM), Markov Chain Analogue Year exponentia...