Traditional statistical as well artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in data, a model developed using traditional or single intelligent technique may not accurately forecast results. Therefore, there is need develop hybridization of an effective predictive model. In this study, we propose forecasting method based on hybrid Arti...

Work-in-progress on the use of a specialized genetic algorithm for training a new type of dynamic artificial neural network is described. The network architecture is completely specified by a list of addresses that are used to connect signal sources to specific artificial synapses, which have both a temporal and spatial significance. The number of different connection patterns is a combinationa...

objective(s): this study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ann) and correlation using experimental data. methods: two-layer perceptron feedforward artificial neural network and backpropagation levenberg-marquardt (bp-lm) training algorithm are used to predict the thermal cond...

One of reasons that researchers in recent years have tried to produce ultrafine grained materials is producing lightweight components with high strength and reliability. There are disparate methods for production of ultra-fine grain materials,one of which is severe plastic deformation method. Severe plastic deformation method comprises different processes, one of which is Parallel tubular chann...

One of reasons that researchers in recent years have tried to produce ultrafine grained materials is producing lightweight components with high strength and reliability. There are disparate methods for production of ultra-fine grain materials,one of which is severe plastic deformation method. Severe plastic deformation method comprises different processes, one of which is Parallel tubular chann...