Now showing items 1-5 of 5
Real-Time Traffic Prediction Improvement through Semantic Mining of Social Networks
Many years of research have yielded computer modeling techniques that can predict the behavior of complex systems, such as traffic speeds in regional transportation systems, with high accuracy. However, the prediction accuracy suffers significantly when non-recurring events, such as traffic accidents, occur in these systems. ...
Accelerating large-scale simulations of cortical neuronal network development
Cultured dissociated cortical cells grown into networks on mult-electrode arrays are used to investigate neuronal network development, activity, plasticity, response to stimuli, the effects of pharmacological agents, etc. We made computational models of such neuronal networks and studied the interplay of individual neuron ...
Design and Qualitative/Quantitative Analysis of Multi-Agent Spatial Simulation Library
Integrating sensor networks in cloud computing gives new opportunities of using as many cloud-computing nodes as necessary to analyze real-time sensor data on the fly. However, most cloud services for parallelization such as OpenMP, MPI, and MapReduce are not always suitable for on-the-fly sensor-data analyses that are ...
SocialLDA:Scalable Topic Modeling in Social Networks
Topical categorization of blogs, documents or other objects that can be tagged with text, improves the experience for end users. Latent Dirichlet allocation (LDA) is a well studied algorithm that discovers latent topics from a corpus of documents so that the documents can then be assigned automatically into appropriate topics. ...
Adaptive Probabilistic Topic Models for Social Networks
Online social networks such as Twitter, LinkedIn, and Facebook generate tremendous amount of text and social interaction data. On one hand, the increasing amount of available information has motivated computational research in social network analysis to understand social structures. On the other hand, annotating, retrieving, ...