Deep Residual Learning-Based Convolutional Variational Autoencoder For Driver Fatigue Classification

Driving under the influence of fatigue often results in uncontrollable vehicle dynamics, which causes severe and fatal accidents. Therefore, early warning on the fatigue onset is crucial to avoid occurrences of such kind of a disaster. In this paper, the authors have investigated a novel semi-supervised convolutional variational autoencoder-based classification approach to classify the state of the driver. A convolutional variational autoencoder is a generative network. The authors have proposed a discriminative model using convolutional variational autoencoders and residual learning. This approach calculates an intermediate loss base on deep features of the network in addition to the label information in training. The loss obtained by this method helps the training to be more effective on the model and leads to better accuracy in driver fatigue classification.  The trained model has managed to classify driver fatigue with higher accuracy (97%) than the other successful models taken into comparison, proving that the proposed method is more practical for computing classification loss for driver fatigue to currently available methods

A performance evaluation of pruning effects on hybrid neural network

In this paper, we explore the pruning effects on a hybrid mode sequential learning algorithmnamely FuzzyARTMAP-prunable Radial Basis Function (FAM-PRBF) that utilizes FuzzyARTMAP to learn a training dataset and Radial Basis Function Network (RBFN) to performregression and classification. The pruning algorithm is used to optimize the hidden layer ofthe RBFN. The experimental results show that FAM-PRBF has successfully reduced thecomplexity and computation time of the neural network.Keywords: pruning; radial basis function network; fuzzy ARTMAP

Artificial Neural Network Application In Environmental Engineering.

The objective of this thesis research is to apply two artificial neural network (ANN) methods, back-propagation neural network (BPN) and radial basis function generalized regression neural network (RBFGRNN) in two environmental engineering case studies to explore their ability to modeling the complex environmental engineering systems. The traditional environmental engineering systems modeling are frequently using the physical-based modeling methods

Installation and testing of server component of the information educational environment of the university on the LMS moodle platform

© Research India Publications 2015. The information educational environment (IEE) of the educational institution is a complex multilevel system and along with the program-methodical, organizational and cultural resources accumulates intellectual and technical potential of the university, the content and activity components of the learners and teachers. In practice, the formation of IEE is actually based on the creation of information technologies and their integration into existing educational environment of the institution. The management of this system is implemented through the dedicated equipment and software. For the successful formation and functioning of the IEE in this paper we consider software products that form the basis of an interactive and web of interaction between students, teachers and all participants of the educational process. Technical capabilities that are provided to users of IEE are analyzed, such as the Apache web server with connected modules PHP, MySQL, Java virtual machine and Red5 Server. The possibility of obtaining results from the interaction of these products, reports on the work of users in webinars, video conferences and web conferences are demonstrated

Morbidity and Mortality Weekly Report

The GMRLN is the largest globally coordinated laboratory network, with 703 laboratories supporting surveillance in 191 countries. During 2010\u20132015, >700,000 serum specimens were tested, and >20,000 viral sequences were reported globally. During the past year, the number of laboratories that participated in molecular proficiency testing increased from 22 to 90. Performance indicators for collection of samples for case confirmation and timeliness of reporting of laboratory results are being met by most laboratoriesn/