[PDF] Top 20 Development of an artificial neural network (ANN) for predicting tribological properties of kenaf fibre reinforced epoxy composites (KFRE)

... Model development was conducted by using two approaches including the application of Andreassen (1985) and Artificial Neural Network (ANN) from Haykin (1994) fatality prediction models in ... See full document

... This study introduces the principle stages of HCR system and the classification process for recognizing a handwritten character. That process will be analyzing using Artificial Neural Network. The ... See full document

... the development of better condition assessment and efficient specification of rehabilitation approaches will have a positive impact on the welfare and safety of the communities served by these types of structures, ... See full document

... At Development Backpropogation Algorithm applied very much for the world Health, Economics, agriculture and others ...[1]-[3]. Artificial Neural Network is an artificial representation ... See full document

... Wear particles, contained in lubricating oil, carry with them important information related to the condition of the corresponding machinery. Microscopic analysis of the wear particles in lubricating oil provides a ... See full document

... a network to train quite ...Today, neural networks discussions are occurring ...hardware development. Currently most neural network development is simply proving that the ... See full document

... We developed and tested a new CAD scheme for brain tumor detection & classification of lungs cancer . To improve the system we tested & examined different CT images. The study showed that the tumor detection ... See full document

... An artificial neuron basically consists of a computing element that performs the weighted sum of the input signal and the connecting ...many artificial neurons in each layer and for a practical case there ... See full document

... in artificial intelligence are now being applied to various fields in society and ...A neural network simulates the pattern recognition capabilities of a biological ...multilayer artificial ... See full document

... the Artificial Neural network (ANN) methodology to control the output voltage of a dc- dc buck converters and also the Artificial Neural network (ANN) methodology to control the ... See full document

... convolutional neural network, trained the eye images with most suitable hyper- parameters, and got the one with best evaluation ...metrics. Artificial Neural Network performed very ... See full document

... regression, artificial neural network (back propagation neural network, generalized regression neural network) and support vector machine (SVM) was ... See full document

... on artificial intelligence algorithms. Neural network optimization based on three basic parameters topology, weights and the learning rate is a powerful ...the network structure and optimized ... See full document

... develop Artificial Neural Network model that will correlate the dependent parameters, the weight loss and drying rate, with temperature, concentration of salt solution and time for osmotic ... See full document

... process. Artificial neural network has ability of tracing a nonlinear form relationship between input and output ...ability, artificial neural networks have widespread application in ... See full document

... more artificial neurons we are getting an artificial neural ...single artificial neuron has almost no usefulness in solving real-life problems the artificial neural networks have ... See full document

... are artificial neural network, Probabilistic Neural network, K means clustering for segmentation and GLCM and SGLDM for texture ... See full document

... 86 Malaysia, due to the difficulty of sampling and the possibility for wading in the water in these areas. New mathematical modelling methods were used to improve the sensitivity and performance of prediction equations ... See full document

... Logits Layer, the final layer of our neural network is the logits layer, which will return the raw values for our predictions. The logit model is a regression model where the dependent variable (DV) is ... See full document

... convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep supervised denoising autoencoder architecture based on extreme learning machine ... See full document