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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書目名稱Artificial Intelligence for Cloud and Edge Computing影響因子(影響力)




書目名稱Artificial Intelligence for Cloud and Edge Computing影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence for Cloud and Edge Computing網(wǎng)絡(luò)公開度




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




書目名稱Artificial Intelligence for Cloud and Edge Computing被引頻次




書目名稱Artificial Intelligence for Cloud and Edge Computing被引頻次學(xué)科排名




書目名稱Artificial Intelligence for Cloud and Edge Computing年度引用




書目名稱Artificial Intelligence for Cloud and Edge Computing年度引用學(xué)科排名




書目名稱Artificial Intelligence for Cloud and Edge Computing讀者反饋




書目名稱Artificial Intelligence for Cloud and Edge Computing讀者反饋學(xué)科排名

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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

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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