Titlebook: Artificial Intelligence and Sustainable Computing for Smart City; First International Arun Solanki,Sanjay Kumar Sharma,Anand Nayyar Confer

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書目名稱Artificial Intelligence and Sustainable Computing for Smart City影響因子(影響力)




書目名稱Artificial Intelligence and Sustainable Computing for Smart City影響因子(影響力)學(xué)科排名




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書目名稱Artificial Intelligence and Sustainable Computing for Smart City網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence and Sustainable Computing for Smart City被引頻次




書目名稱Artificial Intelligence and Sustainable Computing for Smart City被引頻次學(xué)科排名




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書目名稱Artificial Intelligence and Sustainable Computing for Smart City年度引用學(xué)科排名




書目名稱Artificial Intelligence and Sustainable Computing for Smart City讀者反饋




書目名稱Artificial Intelligence and Sustainable Computing for Smart City讀者反饋學(xué)科排名

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Conference proceedings 2021l and emotions analysis for smart cities; smart specialization strategies for smart cities; security in smart cities; advances applications for future smart cities; healthcare in smart cities; machine learning applications in smart cities..

省略

pellagra

Inelasticity

ADJ

The Concerned Public: A Management Challengegestures. The process of gesture recognition includes segregating the videos into frames. An effort has been taken to propose a hand gesture recognition using Convex Hull Segmentation and Bitwise AND approach. Finally, the proposed method demonstrate the viability of the proposed system based on its memory usage in different test environments.

Constitution

The Space Where Relativism and Realism Meet,and exhaustive feature selection (EFS) on 300 lung CT scan nodules to evaluate best feature score. Random Forest (RF) classifier has been used in association with these wrapper methods. The cross-validation score 0.99 was achieved by HSEFS method which serve the best feature selection method among all the above mentioned methods.

Bravura

Sentiment Analysis on the Effect of Trending Source Less News: Special Reference to the Recent Deathnetworks have been used with two layers on the dataset; increasing the value of epochs increases the accuracy to some level and shows an accuracy of 70.58%. Both Random Forest and SVM classifiers showed the same accuracy of 73.52% and the same false-positive and false-negative ratios.

顧客

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