Titlebook: Intelligent Computing & Optimization; Proceedings of the 5 Pandian Vasant,Gerhard-Wilhelm Weber,J. Joshua Tho Conference proceedings 2023 T

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书目名称Intelligent Computing & Optimization影响因子(影响力)




书目名称Intelligent Computing & Optimization影响因子(影响力)学科排名




书目名称Intelligent Computing & Optimization网络公开度




书目名称Intelligent Computing & Optimization网络公开度学科排名




书目名称Intelligent Computing & Optimization被引频次




书目名称Intelligent Computing & Optimization被引频次学科排名




书目名称Intelligent Computing & Optimization年度引用




书目名称Intelligent Computing & Optimization年度引用学科排名




书目名称Intelligent Computing & Optimization读者反馈




书目名称Intelligent Computing & Optimization读者反馈学科排名

参考书目

2367-3370 ing for great scientific and practical values.This book of .Springer Nature. .is another .proof .of Springer’s .outstanding and greatness. on the lively interface of .Smart Computational Optimization, Green ICT, Smart Intelligence and Machine Learning! .It is a .Master Piece. of what our community o

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高射炮

Image Classification for Identifying Social Gathering Typesed that the approaches suggested in this paper improve the performance of the models. Our task was based on four classes of social gathering images. We concluded that the layer-modified VGG16 model with augmentation gives us the best results with a training accuracy of 90.99% and validation accuracy of 87.18%.

异端

SWINC: Secured Wireless Intelligent Network Using Cryptographyappropriately addressed. In this study, we propose a model named SWINC (Secured Wireless Intelligent Network using Cryptography) to overcome the uncertainties associated with authentication. The elliptic curve digital signature technique, one of the most well-known and straightforward cryptographic approaches, is included in our proposed solution.

正式演说

Comparative Performance Assessment of Multi Linear Regression and Artificial Neural Network for Pred was found that ANN model demonstrate performance index (PI) value of 0.996, whereas MLR model exhibits PI value of 0.003. The high PI demonstrates the accuracy of models. Additionally, it was discovered that, when compared to the MLR model, ANN forecasted values more precisely match the observed data.

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