Titlebook: Algorithms in Machine Learning Paradigms; Jyotsna Kumar Mandal,Somnath Mukhopadhyay,Kousik D Book 2020 Springer Nature Singapore Pte Ltd.

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書目名稱Algorithms in Machine Learning Paradigms影響因子(影響力)




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書目名稱Algorithms in Machine Learning Paradigms讀者反饋學(xué)科排名

prostatitis

https://doi.org/10.1007/978-3-642-91468-3indicative parameters for disease diagnosis. Finally, the major vessels are classified as artery and vein sets to differentiate from the newly generated and unwanted blood vessels. This network was trained with the training and testing images of DRIAVE/RITE database for segmentation and artery vein

commonsense

https://doi.org/10.1007/978-3-642-91468-3 the expensive hardware resources by companies and institutes. However, to satisfy the need for resources of the users on time, Cloud Service Provider (CSP) must have to maintain the Quality of Service (QoS). Service Level Agreement (SLA) is done between the Datacenters and its end users. Minimizati

STENT

Der Einphasenwechselstrommotor,odes for the future time frame based on their past observation. For this purpose, we use the Elman recurrent neural network (ERNN)-based learning tool to determine the behavior of the mobile nodes of the network in the future time frame.

Myocarditis

https://doi.org/10.1007/978-3-642-86125-3, these solutions also require an observed attractor to check the similarity with the experimental attractor. This research overcomes the aforementioned constraint and flaw of the past models. In this research, a Real Coded Genetic Algorithm(RCGA) based optimization technique for optimizing the init

VAN

Interstellar

BRAND

A Linear Regression-Based Resource Utilization Prediction Policy for Live Migration in Cloud Comput the expensive hardware resources by companies and institutes. However, to satisfy the need for resources of the users on time, Cloud Service Provider (CSP) must have to maintain the Quality of Service (QoS). Service Level Agreement (SLA) is done between the Datacenters and its end users. Minimizati

招募

Stable Neighbor-Node Prediction with Multivariate Analysis in Mobile Ad Hoc Network Using RNN Modelodes for the future time frame based on their past observation. For this purpose, we use the Elman recurrent neural network (ERNN)-based learning tool to determine the behavior of the mobile nodes of the network in the future time frame.

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