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41#
發(fā)表于 2025-3-28 14:47:12 | 只看該作者
The End of the Cetaceans’ Reigng the learning of nonlinear relationships among diverse latent factors. This approach not only addresses the privacy concerns by preserving the confidentiality of user data but also enables the modeling of temporal dynamics to enhance the accuracy and relevance of recommendations over time.
42#
發(fā)表于 2025-3-28 19:54:23 | 只看該作者
C. Mallet,M. F. Charpin,J. Devauxdemonstrate that DDPG reduces the latency by 41% compared to Deep Q-Network (DQN) and Actor-Critic (AC) algorithm. Our collaborative UAV-D2D model has 16% and 32% lower latency than when only the UAV or D2D works alone.
43#
發(fā)表于 2025-3-28 23:59:24 | 只看該作者
44#
發(fā)表于 2025-3-29 05:06:39 | 只看該作者
45#
發(fā)表于 2025-3-29 08:06:48 | 只看該作者
Anatomy of the Ocular Vasculaturese experimental results of system show that the system can achieve accurate emotion recognition, help users better understand the psychological state of the communication object, and improve the communication environment and quality. In addition, the system can also store communication records and em
46#
發(fā)表于 2025-3-29 14:42:55 | 只看該作者
47#
發(fā)表于 2025-3-29 18:43:41 | 只看該作者
Ocular Delivery of Proteins and Peptides,enters to test the sign language translation system ten times for each person, and the final average accuracy rate was 72%. Through such an interpreting system, it brings a convenient and efficient communication experience to the hearing-impaired person and people who need to communicate with the he
48#
發(fā)表于 2025-3-29 23:10:29 | 只看該作者
49#
發(fā)表于 2025-3-30 01:31:52 | 只看該作者
Convolutional Neural Network Based QoS Prediction with?Dimensional Correlationld a deeper model and learn high-order features. In addition, the parameters of QPCN are significantly reduced, which will reduce the time and energy consumption of inference. The effectiveness of QPCN is validated by experiments on a real-world dataset.
50#
發(fā)表于 2025-3-30 07:19:17 | 只看該作者
Multiple Relays Assisted MEC System for?Dynamic Offloading and?Resource Scheduling with?Energy Harve, we propose an approach called Multi-Relay Assisted Dynamic Computation Offloading (MRADCO) algorithm, which leverages the Lyapunov optimization technique. It is important to note that our proposed algorithm makes decisions solely based on the current state, without requiring the distribution infor
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