Name
Abstract | ||
|---|---|---|
A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infrastructure. A key focus in this paper is on tuning hyper-parameters associated with the deep learning algorithm used to construct the model. We furthe... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSMC.2018.2840341 | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Keywords | Field | DocType |
Training,Streaming media,Mathematical model,Pipelines,Tuning,Optimization,Machine learning | Data mining,Pipeline transport,Hyperparameter,Computer science,Artificial intelligence,Deep learning,Accuracy and precision,Analytics,Cloud storage,Machine learning,Cloud computing,Scalability | Journal |
Volume | Issue | ISSN |
49 | 1 | 2168-2216 |
Citations | PageRank | References |
4 | 0.47 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Usman Yaseen | 1 | 25 | 3.42 |
| Ashiq Anjum | 2 | 333 | 38.33 |
| Omer Rana | 3 | 104 | 15.28 |
| Nikos Antonopoulos | 4 | 10 | 1.04 |