OBJECTIVE Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, rac...

Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data im...

Dynamic linear regression models are used widely in applied econometric research. Most applications employ autoregressive (AR) models, distributed lag (DL) or (ARDL) models. These however, perform poorly for data sets with unknown, complex nonlinear patterns. This paper studies and semiparametric extensions of the dynamic model explores two techniques to allow unknown forms nonlinearities funct...

Pit-in-pit (PIP) excavations in an aquifer–aquitard system likely undergo catastrophic failures under the hydraulic uplift, associated undrained stability problem, however, has not been well analyzed past. To this end, a hypothetical model of PIP braced excavation typical soil layers Shanghai, China is developed using finite element limit analysis (FELA) tool. The FELA solutions safety factors ...

We present a multi-agent architecture for playing the game of Risk which is a multi-player game where the players control armies and try to conquer the world through attacking each others territories. Our solution puts an agent in every territory, and let them negotiate about what actions to prioritize in the phases of placing armies, attack, and fortify. The results of a tournament of 13 parti...

This article revisits the relationship between IT and productivity, and investigates the impact on information technology (IT) investments. Using the MARS techniques, we show that although IT Stock is the greatest predictor variable for productivity (Value Added), it is only significant as an interaction variable, combined with Non-IT Capital, Non-IT Labor, Industry, or Size.

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related. The problem of finding such transformations has been rather neglected in the development of function...

In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to limitations in the industrial data acquisition ...