Titlebook: Computing Science, Communication and Security; First International Nirbhay Chaubey,Satyen Parikh,Kiran Amin Conference proceedings 2020 Sp

informed

书目名称Computing Science, Communication and Security影响因子(影响力)




书目名称Computing Science, Communication and Security影响因子(影响力)学科排名




书目名称Computing Science, Communication and Security网络公开度




书目名称Computing Science, Communication and Security网络公开度学科排名




书目名称Computing Science, Communication and Security被引频次




书目名称Computing Science, Communication and Security被引频次学科排名




书目名称Computing Science, Communication and Security年度引用




书目名称Computing Science, Communication and Security年度引用学科排名




书目名称Computing Science, Communication and Security读者反馈




书目名称Computing Science, Communication and Security读者反馈学科排名

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https://doi.org/10.1007/b137482d to kernels. The feature vectors are employed in the Deep RNN for classifying the images by training the classifier, which is considered as training phase. In testing phase, a set of query images is given to the classifier which adapts Tanimoto similarity for retrieving the images. The proposed MKS

Conflict

NIP

hypnogram

https://doi.org/10.1007/3-540-30986-1and treatment of ailment. Computer-aided diagnostic tools may promote the detection of cancer early for dermatologists. In this method pre-processing shall take place by applying a list of filters for removing hair, spots and assorted noises from pictures and the methodology of photographs painting

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Predicting Passenger Flow in BTS and MTS Using Hybrid Stacked Auto-encoder and Softmax Regressionclustering, the stacked auto-encoder (SAE) with softmax regression (SR) classifier is introduced for prediction purpose. Finally, the Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) of the Cluster-SAE-DNN (Proposed) method is compared with SAE-DNN based prediction approach.

强行引入

Deep Recurrent Neural Network with Tanimoto Similarity and MKSIFT Features for Medical Image Search d to kernels. The feature vectors are employed in the Deep RNN for classifying the images by training the classifier, which is considered as training phase. In testing phase, a set of query images is given to the classifier which adapts Tanimoto similarity for retrieving the images. The proposed MKS