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correspondent

Performer: A Resource Demand Forecasting Method for Data Centers,nd to schedule tasks. To cope with the huge number of workloads in a data center, workloads are usually clustered first and then prediction is conducted for each cluster. However, training different models for different clusters separately reduces the overall utilization of the data in the data cent

PALSY

,Optimizing Video QoS for eMBMS Users in the Internet of Vehicles,aced growth of the automotive industry and communications technologies. At the same time, with the rapid development of in-vehicle video, the development of broadcasting business has been driven. The 3GPP standardization group has suggested the evolved Multimedia Broadcast Multicast Service (eMBMS),

TATE

,Huffman Tree Based Multi-resolution Temporal Convolution Network for Electricity Time Series Predicand. However, most existing methods cannot capture the complicated structure of electricity time series, and make personalized suggestions on electricity purchasing scheme. The main challenge lies in the periodicity and instability of electricity time series. To capture the global and local features

Incisor

Deep Learning-Based Autonomous Cow Detection for Smart Livestock Farming,ought the autonomous robotic system to the smart farming that enhance productivity and efficiency. Therefore, a YOLOv4-SAM was proposed to achieve high detection precision of cow body parts in long-term complex scenes. The proposed YOLOv4-SAM consists of two components: YOLOv4 is for multi-scale fea

亲爱

Bedeutung der Lungenfunktionsdiagnostik,nhance the texture features. The results of the experiment show that the average accuracy, the average specificity and the average sensitivity of the improved algorithm increase by 9.2%, 6.4% and 6.5% respectively. The improved algorithm is effective in glaucoma fundus image classification.

concise

,Decision Tree Fusion and Improved Fundus Image Classification Algorithm,nhance the texture features. The results of the experiment show that the average accuracy, the average specificity and the average sensitivity of the improved algorithm increase by 9.2%, 6.4% and 6.5% respectively. The improved algorithm is effective in glaucoma fundus image classification.