Abstract Raman spectroscopy provides a vibrational profile of the molecules and thus can be used to uniquely identify different kinds materials. This sort molecule fingerprinting has led widespread application spectrum in various fields like medical diagnosis, forensics, mineralogy, bacteriology, virology, etc. Despite recent rise spectra data volume, there not been any significant effort devel...

Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve goal. Instead of training protecting specific image, train on an image dataset a...

estimation (forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. thus, accuracy of the estimation is highly desirable. hibrid regression neural network is an approach proposed in this paper to obtain better fitness in comparison with regression analysis and the neural network methods. comparing the estimated resul...

Multi-motor synchronous system is a multi-input multi-output, nonlinear and high coupling control system. The neural network generalized inverse system can realize the linearization and decoupling of the nonlinear control. Local minima, irrationality learning rate and over learning easily occur in traditional feedforward neural networks. To overcome the problems, it put forward a single hidden-...

Abstract — This paper presents an algorithm for feature extraction from a bark image set based on parameter of generalized Gaussian density (GGD) model and color angles in different color space. The extracted features such as the scale parameter and the shape parameter of GGD for image will be employed for classification. The radial basis probabilistic neural networks (RBPNN) and supporting vec...

in this study, artificial neural network was used to predict the surface tension of 20 hydrocarbon mixtures. experimental data was divided into two parts (70% for training and 30% for testing). optimal configuration of the network was obtained with minimization of prediction error on testing data. the accuracy of our proposed model was compared with four well-known empirical equations. the arti...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

water quality assessment provides a scientific basis for water resources development and management. this case study proposes a factor analysis- hopfield neural network model (fhnn) based on factor analysis method and hopfield neural network method. the results showed that the factor analysis (fa) technique was introduced to identify important water quality parameters. results revealed that bio...