FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3213787142> ?p ?o ?g. }

• W3213787142 endingPage "40" @default.
• W3213787142 startingPage "31" @default.
• W3213787142 abstract "Future generation networks such as millimeter-wave LAN, broadband wireless access systems, and 5th or 6th generation (5G/6G) networks demand more security, low latency with more reliable standards and communication capacity. Efficient congestion control is considered one of the key elements of 5G/6G technology that allows the operators to run various network instances using a single infrastructure for a better quality of services. Artificial intelligence (AI) and machine learning (ML) are playing an essential role in reconfiguring and optimizing the performance of a 5G/6G wireless network due to a vast amount of data. A smart decision-making mechanism is required for the incoming network traffic to ensure load balancing, restrict network slice failure and provide alternate slices in case of slice failure or overloading. To circumvent these issues, a hybrid deep learning-enabled efficient congestion control mechanism is proposed in this paper. This hybrid deep learning model consists of long short term memory (LSTM) and support vector machine (SVM). The applicability of the proposed model is validated by simulating for one week using multiple unknown devices, slice failure conditions, and overloading conditions. An overall accuracy rate of 93.23% is calculated for the proposed hybrid model that reflects the applicability. Apart from this, other performance metrics such as specificity, recall, time consumption, varying training, test sets, true-false rates, and f-score were used for the performance evaluation purposes of the proposed model." @default.
• W3213787142 created "2021-11-22" @default.
• W3213787142 creator A5011327434 @default.
• W3213787142 creator A5018980806 @default.
• W3213787142 creator A5020009700 @default.
• W3213787142 creator A5027946901 @default.
• W3213787142 creator A5078275221 @default.
• W3213787142 creator A5079785687 @default.
• W3213787142 date "2022-01-01" @default.
• W3213787142 modified "2023-10-14" @default.
• W3213787142 title "Efficient and reliable hybrid deep learning-enabled model for congestion control in 5G/6G networks" @default.
• W3213787142 cites W2128145324 @default.
• W3213787142 cites W2528613733 @default.
• W3213787142 cites W2560828602 @default.
• W3213787142 cites W2564243131 @default.
• W3213787142 cites W2610135452 @default.
• W3213787142 cites W2765603629 @default.
• W3213787142 cites W2781593575 @default.
• W3213787142 cites W2794194229 @default.
• W3213787142 cites W2808683316 @default.
• W3213787142 cites W2889919018 @default.
• W3213787142 cites W2891204710 @default.
• W3213787142 cites W2896394395 @default.
• W3213787142 cites W2902262352 @default.
• W3213787142 cites W2918764357 @default.
• W3213787142 cites W2922164811 @default.
• W3213787142 cites W2939745396 @default.
• W3213787142 cites W2969501197 @default.
• W3213787142 cites W2981556814 @default.
• W3213787142 cites W3006210340 @default.
• W3213787142 cites W3010676594 @default.
• W3213787142 cites W3021789920 @default.
• W3213787142 cites W3034903324 @default.
• W3213787142 cites W3082748375 @default.
• W3213787142 cites W3083985116 @default.
• W3213787142 cites W3097270768 @default.
• W3213787142 cites W3099769178 @default.
• W3213787142 cites W3110411590 @default.
• W3213787142 cites W3119071788 @default.
• W3213787142 cites W3132920691 @default.
• W3213787142 cites W3134395502 @default.
• W3213787142 cites W3137511895 @default.
• W3213787142 cites W3139073871 @default.
• W3213787142 cites W3143379305 @default.
• W3213787142 cites W3156592846 @default.
• W3213787142 doi "https://doi.org/10.1016/j.comcom.2021.11.001" @default.
• W3213787142 hasPublicationYear "2022" @default.
• W3213787142 type Work @default.
• W3213787142 sameAs 3213787142 @default.
• W3213787142 citedByCount "16" @default.
• W3213787142 countsByYear W32137871422022 @default.
• W3213787142 countsByYear W32137871422023 @default.
• W3213787142 crossrefType "journal-article" @default.
• W3213787142 hasAuthorship W3213787142A5011327434 @default.
• W3213787142 hasAuthorship W3213787142A5018980806 @default.
• W3213787142 hasAuthorship W3213787142A5020009700 @default.
• W3213787142 hasAuthorship W3213787142A5027946901 @default.
• W3213787142 hasAuthorship W3213787142A5078275221 @default.
• W3213787142 hasAuthorship W3213787142A5079785687 @default.
• W3213787142 hasConcept C108037233 @default.
• W3213787142 hasConcept C108583219 @default.
• W3213787142 hasConcept C119857082 @default.
• W3213787142 hasConcept C154945302 @default.
• W3213787142 hasConcept C158379750 @default.
• W3213787142 hasConcept C195563490 @default.
• W3213787142 hasConcept C26517878 @default.
• W3213787142 hasConcept C31258907 @default.
• W3213787142 hasConcept C38652104 @default.
• W3213787142 hasConcept C41008148 @default.
• W3213787142 hasConcept C555944384 @default.
• W3213787142 hasConcept C76155785 @default.
• W3213787142 hasConcept C82876162 @default.
• W3213787142 hasConceptScore W3213787142C108037233 @default.
• W3213787142 hasConceptScore W3213787142C108583219 @default.
• W3213787142 hasConceptScore W3213787142C119857082 @default.
• W3213787142 hasConceptScore W3213787142C154945302 @default.
• W3213787142 hasConceptScore W3213787142C158379750 @default.
• W3213787142 hasConceptScore W3213787142C195563490 @default.
• W3213787142 hasConceptScore W3213787142C26517878 @default.
• W3213787142 hasConceptScore W3213787142C31258907 @default.
• W3213787142 hasConceptScore W3213787142C38652104 @default.
• W3213787142 hasConceptScore W3213787142C41008148 @default.
• W3213787142 hasConceptScore W3213787142C555944384 @default.
• W3213787142 hasConceptScore W3213787142C76155785 @default.
• W3213787142 hasConceptScore W3213787142C82876162 @default.
• W3213787142 hasFunder F4320323722 @default.
• W3213787142 hasLocation W32137871421 @default.
• W3213787142 hasOpenAccess W3213787142 @default.
• W3213787142 hasPrimaryLocation W32137871421 @default.
• W3213787142 hasRelatedWork W2611989081 @default.
• W3213787142 hasRelatedWork W2731899572 @default.
• W3213787142 hasRelatedWork W3133147449 @default.
• W3213787142 hasRelatedWork W3215138031 @default.
• W3213787142 hasRelatedWork W4230611425 @default.
• W3213787142 hasRelatedWork W4294635752 @default.
• W3213787142 hasRelatedWork W4304166257 @default.
• W3213787142 hasRelatedWork W4327774331 @default.
• W3213787142 hasRelatedWork W4375867731 @default.

• next