Titlebook: Distributed Computing and Intelligent Technology; 19th International C Anisur Rahaman Molla,Gokarna Sharma,Sanjay Rawat Conference proceedi

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書(shū)目名稱Distributed Computing and Intelligent Technology影響因子(影響力)




書(shū)目名稱Distributed Computing and Intelligent Technology影響因子(影響力)學(xué)科排名




書(shū)目名稱Distributed Computing and Intelligent Technology網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Distributed Computing and Intelligent Technology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Distributed Computing and Intelligent Technology被引頻次




書(shū)目名稱Distributed Computing and Intelligent Technology被引頻次學(xué)科排名




書(shū)目名稱Distributed Computing and Intelligent Technology年度引用




書(shū)目名稱Distributed Computing and Intelligent Technology年度引用學(xué)科排名




書(shū)目名稱Distributed Computing and Intelligent Technology讀者反饋




書(shū)目名稱Distributed Computing and Intelligent Technology讀者反饋學(xué)科排名

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Conference proceedings 2023s held in Bhubaneswar, India, in January 2023..The 20 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are organized in the following topical sections:?Invited Talks;?Distributed Computing; Intelligent Technology..

擋泥板

惰性女人

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Roger Jankowski M.D., Ph.D., PUPHP address database is partitioned into a different table based on first . bits of IP address, then a variant of trie approach is proposed to find the next hop. The proposed lookup approach shows 64.46% and 94.32% improvement than binary trie and BST implementations.

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作繭自縛

The Primary Nose and Palate in Evolutionrged frames in surveillance videos. Firstly, the motion residuals are computed from the videos sequences to extract the video forgery footprints, which are generated due to the manipulation operations. Secondly, the computed motion residuals are fed to VGG-16 network for detecting authentic and forged frames in a surveillance video.

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The Toba supervolcanic eruption: , used for baseline comparison. The proposed DCNN approach outperformed all the baselines chosen with an improvement of 2.1% in the f1-score. This study shows that deep learning would be better for building models capable of classifying landslides on real-world datasets.