Classifier combining techniques have become popular for improving weak classifiers in recent years. The random subspace method (RSM) is an efficient classifier combining technique that can improve classification performance of weak classifiers for the small sample size (SSS) problems. In RSM, the feature subsets are randomly selected and the resulting datasets are used to train classifiers. How...

Optimization plays a key role in environmental engineering parameters since the best system performance mainly is on optimum point or optimum range. The majority of wastewater treatment processes are multi-variable and optimization through the classical method is inflexible, unreliable and time-consuming. Thus, response surface methodology (RSM), as a very efficient design and widely used techn...

This paper introduces an original 2-valued semantics for Normal Logic Programs (NLP), important on its own. Nevertheless, its name draws attention to that it is inspired by and generalizes Stable Model semantics (SM). The definitional distinction consists in the revision of one feature of SM, namely its treatment of odd loops over default negation. This single revised aspect, addressed by means...

RSM is a replacement sensor model that replaces with high fidelity all aspects of the original sensor model for virtually all imaging sensors. This includes the ground-to-image relationship, adjustability, and rigorous error propagation. RSM can do so for both satellite and airborne platforms as well as for different sensor modalities (optical and SAR). This paper provides a general introductio...

Probabilistic Finite Element (FE) models have recently been developed to assess the impact of experimental variability present in knee wear simulator on predicted Total Knee Replacement (TKR) mechanics by determining the performance envelope of joint kinematics and contact mechanics. The gold standard for this type of analysis is currently the Monte Carlo method, however, this requires a larger...

Various modeling techniques were used to understand fluidized bed granulation using a two-step approach. First, Plackett-Burman design (PBD) was used to identify the high-risk factors. Then, Box-Behnken design (BBD) was used to analyze and optimize those high-risk factors. The relationship between the high-risk input variables (inlet air temperature X1, binder solution rate X3, and binder-to-po...

Response Surface Methodology (RSM) searches for the input combination that optimizes the simulation output. RSM treats the simulation model as a black box. Moreover, this paper assumes that simulation requires much computer time. In the first stages of its search, RSM locally fits first-order polynomials. Next, classic RSM uses steepest descent (SD); unfortunately, SD is scale dependent. Theref...

Rapeseed meal (RSM) was subjected to different physical or chemical pretreatments to decrease residual, hard to degrade carbohydrates and to improve fermentability of RSM polysaccharides. Next, these pretreated samples were in vitro digested and fermented, with or without the addition of commercial pectinolytic enzymes. Remaining carbohydrates were quantified, and two physical characteristics w...

Modeling and optimization were performed to enhance production of lactase through submerged fermentation by Bacillus subtilis VUVD001 using artificial neural networks (ANN) and response surface methodology (RSM). The effect of process parameters namely temperature (°C), pH, and incubation time (h) and their combinational interactions on production was studied in shake flask culture by Box-Behnk...