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Full-Text Articles in Computer Engineering
Novel H.265 Video Traffic Prediction Models Using Artificial Neural Networks, Collin Daly
Novel H.265 Video Traffic Prediction Models Using Artificial Neural Networks, Collin Daly
Honors College Theses
In this work, we propose the use of non-linear, autoregressive neural network models for predicting video frame sizes. This model utilizes H.265 encoded video traces as inputs and the predicted future frame sizes as outputs. This model is developed to predict ultra-high definition video frame encoded with H.265 within IP networks. The video I, P, and B frames are predicted separately to improve model prediction accuracy. This approach is verified in MATLAB using various H.265 video traces. The results indicate that the proposed models were able to predict the video traffic fairly accurately.
Determining Unique Agents By Evaluating Web Form Interaction, Ben Cooley
Determining Unique Agents By Evaluating Web Form Interaction, Ben Cooley
Electronic Theses and Dissertations
Because of the inherent risks in today’s online activities, it becomes imperative to identify a malicious user masquerading as someone else. Incorporating biometric analysis enhances the confidence of authenticating valid users over the Internet while providing additional layers of security with no hindrance to the end user. Through the analysis of traffic patterns and HTTP Header analysis, the detection and early refusal of robot agents plays a great role in reducing fraudulent login attempts.