Titlebook: Advanced Machine Learning Approaches in Cancer Prognosis; Challenges and Appli Janmenjoy Nayak,Margarita N. Favorskaya,Manohar Mi Book 2021

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书目名称Advanced Machine Learning Approaches in Cancer Prognosis影响因子(影响力)




书目名称Advanced Machine Learning Approaches in Cancer Prognosis影响因子(影响力)学科排名




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书目名称Advanced Machine Learning Approaches in Cancer Prognosis网络公开度学科排名




书目名称Advanced Machine Learning Approaches in Cancer Prognosis被引频次




书目名称Advanced Machine Learning Approaches in Cancer Prognosis被引频次学科排名




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书目名称Advanced Machine Learning Approaches in Cancer Prognosis年度引用学科排名




书目名称Advanced Machine Learning Approaches in Cancer Prognosis读者反馈




书目名称Advanced Machine Learning Approaches in Cancer Prognosis读者反馈学科排名

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Lecture Notes in Computer Sciencesing short-breaths will be the ease of usage and compatibility of the network. The prediction can lead to earliest diagnosis possible when the concerned person identifies unusual breathing habits. The prediction can also propose other tests to be done if required. The creation of networks for both w

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Lecture Notes in Computer Scienced pathological observations of the thyroid disease were also surveyed. The forty-two machine learning algorithms are compared to find the top five best classifiers to predict whether a given patient is suffering from hypothyroidism, hyperthyroidism or is absolute normal. The data source has been tak

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Bryan Pauken,Mudit Pradyumn,Nasseh Tabriziared with large collection of databases with the replacement of sigmoid activation function. Probabilistic neural network is used to describe nonlinear statement limits which further leads to Bayes optimal and also all the function which bear same properties as well. Any input data or algorithm can

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https://doi.org/10.1007/978-3-030-96282-1G16, EfficientNet, Dense Net121, ResNext50 in the large-scale cancer image data classification setting. Our main contribution is to focus on the high-level accuracy because these deep learning algorithms have the capability of transfer learning with image instant segmentation.

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