![]() Speech recognition is largely taking advantage of deep learning, showing that substantial benefits can be obtained by modern Recurrent Neural Networks (RNNs). The work also provides valuable analysis results and tools to compare different predictors and strike the best balance among the performance measures. The experimental results witness remarkable variability in prediction performance among different apps categories. For the sake of reproducibility and relevance to modern traffic, all evaluations are conducted leveraging two real human-generated mobile traffic datasets including different categories of mobile apps. We compare the results with both Markovian and classic Machine Learning approaches, showing increased performance with respect to state-of-the-art predictors (high-order Markov chains and Random Forest Regressor). We discuss and experimentally evaluate the prediction effectiveness of the provided approaches also assessing the benefits of a number of design choices such as memory size or multimodality, investigating performance trends at packet level focusing on the head and the tail of biflows. In this work, we investigate and specialize a set of architectures selected among Convolutional, Recurrent, and Composite Neural Networks, to predict mobile-app traffic at the finest (packet-level) granularity. ![]() The recent investigation and success of sophisticated Deep Learning algorithms is now providing mature tools to face this challenging but promising goal. This much harder problem (whose solution extends trivially to the aggregated prediction) allows a finer-grained knowledge and wider possibilities of exploitation. Very limited attempts can instead be found tackling prediction at packet-level granularity. A significant corpus of work has so far focused on aggregated behavior, e.g., considering traffic volumes observed over a given time interval. To the north and the Atlantic Ocean to the south, Augusta is a thriving communityīuilt on a solid foundation of local pride and artistic eccentricity.The prediction of network traffic characteristics helps in understanding this complex phenomenon and enables a number of practical applications, ranging from network planning and provisioning to management, with security implications as well. Serving as a halfway point between the Appalachian Mountains Live music and performances, unique food and cultural offerings, top-rated schoolsĪugusta University’s primary campuses in Augusta are situated on the southern banks Retaining access to the rich resources of Georgia’s second-largest city, including Additionally, opportunities to impart knowledgeĪnd application abound in the Hull College of Business Katherine Reese Pamplin College of Arts, Humanities, and Social Sciences the Colleges of Education, Science and Mathematics, Allied Health Sciences, and Nursing and The Graduate School.Īt Augusta, our faculty and staff have the unique opportunity to be part of an intimateĬampus experience with small-class sizes and a diverse community of learners, while ![]() Medical College of Georgia, the state’s only dental school, The Dental College of Georgia, and the state-of-the art School of Computer and Cyber Sciences located in the Georgia Cyber Center. Our colleges and schools include the nation’s eighth-largest medical school, the Practice, and provide support to our students and patients across 8 colleges and 2 schools, at our 2 libraries, and in our world class medical center. At Augusta University, faculty and staff work together to teach, conduct research,
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