Flood Routing Calculation with ANN, SVM, GPR, and RTE Methods

Anomaly Detection with ANN and SVM for Telemedicine Networks

SHORT COMMUNICATION Prediction of Transmembrane Regions of -Barrel Proteins Using ANN- and SVM-Based Methods

This article describes a method developed for predicting transmembrane -barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of theANN-basedmethod improved significantly, from70.4% to 80....

Flood routing modelling with Artificial Neural Networks

For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HECRAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resoluti...

Directed Flood-Routing Framework

The Directed Flood-Routing Framework (DFRF) for wireless sensor networks that allows the rapid development of application specific routing protocols based on directed flooding is introduced in this paper. Flood-routing protocols are probabilistic methods that make only the best effort to route data packets. The presented family of protocols can route regular sized data packets via broadcast mes...

Application of SVM and ANN for intrusion detection

The popularization of shared networks and Internet usage demands increases attention on information system security, particularly on intrusion detection. Two data mining methodologies—Arti6cial Neural Networks (ANNs) and Support Vector Machine (SVM) and two encoding methods—simple frequency-based scheme and tf×idf scheme are used to detect potential system intrusions in this study. Our results ...