Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide ...

drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. identification of resistant phenotype is very important because it can lead to effective treatment plan. there is an interest in developing classifying models of resistance phenotype based on the multivariate data. we have investigated a vibrational spectroscopic approach in order to characterize a sen...

objective(s): a fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. artificial neural networks (anns) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. we proposed a structure that obtains binding energy using physicochemical molecular descriptions of the se...

OBJECT The authors describe the artificial neural network (ANN) as an innovative and powerful modeling tool that can be increasingly applied to develop predictive models in neurosurgery. They aimed to demonstrate the utility of an ANN in predicting survival following traumatic brain injury and compare its predictive ability with that of regression models and clinicians. METHODS The authors de...

in this article a comparative study for modeling and optimization of 2-methylpropane-2-thiol removal from contaminated soil by ultrasound is investigated. central composite design (ccd) and artificial neural network (ann) were utilized and compared to each other in order to obtain appropriate predicting model with respect to sonication power (w), sonication time (min) and water/reactor volume r...

This paper is focused on developing more efficient computational schemes for modeling in biochemical processes. A theoretical framework for estimation of process kinetic rates based on different temporal (time accounting) Artificial Neural Network (ANN) architectures is introduced. Three ANNs that explicitly consider temporal aspects of modeling are exemplified: i) Recurrent Neural Network (RNN...

This paper describes a new approach of modeling visual speech, based on an artificial neural network (ANN). The network architecture makes possible a fusion of linguistic expert knowledge into the ANN. Goal is the development of a computer animation program as a training aid for learning lip-reading. The current PC version allows a synchronization of the animation program with a special stand-a...

Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK. Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA. Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michi...

Artificial Neural Network (ANN) has been used in nonlinear systems modeling and simulation. One of the most useful and interesting factors of ANNs is forecasting. This paper discusses the application of ANNs to predict the long range energy consumption for a country. In this study the long-term energy consumption for the years ahead is predicted, exploiting ANN computational speed, ability to h...

abstract from longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. in this study by using observational data, was evaluated empirical, artificial neural network (ann) and ca-active neuro-fuzzy inference system (canfis) models in estimation of runoff. for this purpose, by using climatic and phy...