Titlebook: Big Data Technologies and Applications; 10th EAI Internation Zeng Deze,Huan Huang,Naveen Chilamkurti Conference proceedings 2021 ICST Insti

Kidnap

Do College Students Adapt to Personal Learning Environment (PLE)? A Single-Group Studyironment, which is manifested in three aspects: perceived attention discontinuity, lingering thought, social media notification.; Under simple training or natural conditions, students have poor adaptability in the personal learning environment, and their behavior perception and behavior adjustment l

溫順

A Big Data Intelligence Marketplace and Secure Analytics Experimentation Platform for the Aviation Iditional sophisticated features, while also new techniques and tools are frequently introduced as a result of the undergoing research activities. Nevertheless, despite the large efforts and investments on research and innovation, the Big Data technologies introduce also a number of challenges to its

choroid

NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systemshe following four existing benchmark NIDS datasets: UNSW-NB15, BoT-IoT, ToN-IoT, and CSE-CIC-IDS2018. We have used the raw packet capture files of these datasets, and converted them to the NetFlow format, with a common feature set. The benefits of using NetFlow as a common format include its practic

PARA

泥瓦匠

Spectrum Sensing and Prediction for 5G Radioa single resource block (RB). The results have been obtained for two different datasets: the 5G downlink signal with representation of daily traffic fluctuations and the sensor-network uplink signal characteristic for IoT. The obtained results prove DL algorithms usefulness for spectrum occupancy pr

thalamus

Epidural-Space

制定法律

1867-8211 nd communicating networks, wireless communication security, green wireless network architectures and IoT based applications..978-3-030-72801-4978-3-030-72802-1Series ISSN 1867-8211 Series E-ISSN 1867-822X

煤渣

Further Developments and Applications,he detectors is significantly affected by different noise models. In any case, the detection probability of KNN algorithm is the highest; the performance of MF is much better than ED under different noise models; KNN is not sensitive to noise uncertainty; MF has better performance on noise uncertainty which makes ED performance decline fleetly.

不能約

Performance Evaluation of Energy Detection, Matched Filtering and KNN Under Different Noise Modelshe detectors is significantly affected by different noise models. In any case, the detection probability of KNN algorithm is the highest; the performance of MF is much better than ED under different noise models; KNN is not sensitive to noise uncertainty; MF has better performance on noise uncertainty which makes ED performance decline fleetly.