[HTML][HTML] A deep learning approach to predict and optimise energy in fish processing industries
The fish processing sector is experiencing increased pressure to reduce its energy
consumption and carbon footprint as a response to (a) an increasingly stringent energy …
consumption and carbon footprint as a response to (a) an increasingly stringent energy …
AI-native interconnect framework for integration of large language model technologies in 6G systems
The evolution towards 6G architecture promises a transformative shift in communication
networks, with artificial intelligence (AI) playing a pivotal role. This paper delves deep into …
networks, with artificial intelligence (AI) playing a pivotal role. This paper delves deep into …
Mobile traffic prediction in consumer applications: a multimodal deep learning approach
W Jiang, Y Zhang, H Han, Z Huang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Mobile traffic prediction is an important yet challenging problem in consumer applications
because of the dynamic nature of user behavior, varying application quality of service (QoS) …
because of the dynamic nature of user behavior, varying application quality of service (QoS) …
Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps
Significant in lifestyle have reshaped the Internet landscape, resulting in notable shifts in
both the magnitude of Internet traffic and the diversity of apps utilized. The increased …
both the magnitude of Internet traffic and the diversity of apps utilized. The increased …
Zero-setup Intermediate-rate Communication Guarantees in a Global Internet
Network-targeting volumetric DDoS attacks remain a major threat to Internet communication.
Unfortunately, existing solutions fall short of providing forwarding guarantees to the …
Unfortunately, existing solutions fall short of providing forwarding guarantees to the …
HSeq2Seq: Hierarchical graph neural network for accurate mobile traffic forecasting
Hierarchical graph neural networks (HGNNs) provide a feasible method for modeling
complex spatiotemporal dependencies during mobile traffic forecasting. However, most …
complex spatiotemporal dependencies during mobile traffic forecasting. However, most …
Spatial-temporal dynamic graph convolutional neural network for traffic prediction
W Xiao, X Wang - IEEE Access, 2023 - ieeexplore.ieee.org
Due to the complexity and dynamics of transportation systems, traffic prediction has become
a challenging task. The accuracy of prediction is influenced by the spatial-temporal …
a challenging task. The accuracy of prediction is influenced by the spatial-temporal …
[HTML][HTML] Network traffic prediction by learning time series as images
Network traffic prediction is crucial for cost-effective network management, resource
allocation, and security in the emergent software-defined and zero-touch networks. Machine …
allocation, and security in the emergent software-defined and zero-touch networks. Machine …
Enhancing the effluent prediction accuracy with insufficient data based on transfer learning and LSTM algorithm in WWTPs
Y Yu, Y Chen, S Huang, R Wang, Y Wu, H Zhou… - Journal of Water …, 2024 - Elsevier
Recently, there has been rapid advancement in artificial intelligence (AI), revolutionizing
numerous industries. But the application of AI in the wastewater treatment plants (WWTPs) is …
numerous industries. But the application of AI in the wastewater treatment plants (WWTPs) is …
[HTML][HTML] Hybrid learning strategies for multivariate time series forecasting of network quality metrics
This work addresses the challenge of forecasting temporal metrics that characterize cellular
traffic behavior. The ultimate goal is to provide network operators with a valuable tool for …
traffic behavior. The ultimate goal is to provide network operators with a valuable tool for …