Titlebook: Artificial Intelligence for Cloud and Edge Computing; Sanjay Misra,Amit Kumar Tyagi,Lalit Garg Book 2022 The Editor(s) (if applicable) and

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书目名称Artificial Intelligence for Cloud and Edge Computing影响因子(影响力)




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




书目名称Artificial Intelligence for Cloud and Edge Computing网络公开度




书目名称Artificial Intelligence for Cloud and Edge Computing网络公开度学科排名




书目名称Artificial Intelligence for Cloud and Edge Computing被引频次




书目名称Artificial Intelligence for Cloud and Edge Computing被引频次学科排名




书目名称Artificial Intelligence for Cloud and Edge Computing年度引用




书目名称Artificial Intelligence for Cloud and Edge Computing年度引用学科排名




书目名称Artificial Intelligence for Cloud and Edge Computing读者反馈




书目名称Artificial Intelligence for Cloud and Edge Computing读者反馈学科排名

竖琴

PIZZA

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https://doi.org/10.1007/978-3-8348-9151-8onic health record (EHR) ends. Also, an artificial intelligence (AI) approach, which is referred to as subjective logic-based belief or reasoning model, was used towards measuring the trustworthiness of the medical personnel. Metrics such as the average system computation time otherwise referred to

WAG

https://doi.org/10.1007/978-3-8348-9151-8lligence in a distributed environment. The proposed model seeks to improve resource utilization to fulfill grid users’ demands. The resources’ costs can change during the auction based on their processing capabilities, helping the proposed model to increase resource usage. Also, resources can increa

Lipoma

Cabinet

https://doi.org/10.1007/978-3-8348-9151-8 matrix in terms of accuracy was used to evaluate the system. The result of the experiment shows an 85% accuracy which outperformed other methods that have been suggested and compared within the literature. However, this study proves to be relevant and can be adopted for better efficiency in IoT and

IST

https://doi.org/10.1007/978-3-8348-9493-9ility of the framework is tested in real-time analysis of healthcare monitoring of patients’ data for automatic managing of body temperature, blood glucose, and blood pressure. The integration of the system shows improvement in patients’ health monitoring situations. The system alerts the physicians

FER

https://doi.org/10.1007/978-3-8348-9493-9length by the able utilization of the genetic algorithm. We have carried out various statistical tests to uphold the strength of the generated key sequences. Amongst them, hybrid model generated a higher range of 13,425 subsequences, a chi-square value of 2.862 which is way better than a critical st