Titlebook: Artificial Intelligence and Sustainable Computing; Proceedings of ICSIS Manjaree Pandit,M. K. Gaur,Sandeep Kumar Conference proceedings 202

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书目名称Artificial Intelligence and Sustainable Computing影响因子(影响力)




书目名称Artificial Intelligence and Sustainable Computing影响因子(影响力)学科排名




书目名称Artificial Intelligence and Sustainable Computing网络公开度




书目名称Artificial Intelligence and Sustainable Computing网络公开度学科排名




书目名称Artificial Intelligence and Sustainable Computing被引频次




书目名称Artificial Intelligence and Sustainable Computing被引频次学科排名




书目名称Artificial Intelligence and Sustainable Computing年度引用




书目名称Artificial Intelligence and Sustainable Computing年度引用学科排名




书目名称Artificial Intelligence and Sustainable Computing读者反馈




书目名称Artificial Intelligence and Sustainable Computing读者反馈学科排名

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Impact of Online Classes Using Machine Learning Algorithms: Estimation, Classification, and Predicts of both UG and PG. The dataset is analyzed using the various machine learning algorithms K-Nearest Neighbor, Decision Tree, Support Vector Machine, Random Forest, Neural Network, Naïve Bayes, and Logistic Regression to predict the severity of the student‘s difficulties due to online classes. The P

招致

GROVE

Transfer Learning Approach to Detect and Predict the Malaria from Blood Cell Images,el which achieves a 96% accuracy rate in binary identifying images of infected and uninfected cells. The proposed Hybrid model is a combination of Visual Geometry Group (VGG) and Capsule Network. This article‘s findings will improve malaria diagnosis, which will help address issues with treatment co

外科医生

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竖琴

Artificial Intelligence and Machine Learning for Climate Change Mitigation and Adaptation,nformation and climate communication. An example of a climate optimism recommendation engine is described to demonstrate the potential of AI. To encourage the use of AI for climate action, this report offers suggestions to organizations, authorities and researchers in the field of artificial intelli

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A Model to Identify the Impairment Caused by Smoking to the Oral Cavity, of 70:30 where 70% data is used for training the CNN and the remaining 30% data is used for validating the model. The developed CNN model showed training and test accuracy of 0.9995 and 1.00 respectively. On the other hand, the training and test loss value computed for the CNN model is 0.0151 and 0

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