Titlebook: Computational Intelligence in Data Mining—Volume 1; Proceedings of the I Himansu Sekhar Behera,Durga Prasad Mohapatra Conference proceeding

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书目名称Computational Intelligence in Data Mining—Volume 1影响因子(影响力)




书目名称Computational Intelligence in Data Mining—Volume 1影响因子(影响力)学科排名




书目名称Computational Intelligence in Data Mining—Volume 1网络公开度




书目名称Computational Intelligence in Data Mining—Volume 1网络公开度学科排名




书目名称Computational Intelligence in Data Mining—Volume 1被引频次




书目名称Computational Intelligence in Data Mining—Volume 1被引频次学科排名




书目名称Computational Intelligence in Data Mining—Volume 1年度引用




书目名称Computational Intelligence in Data Mining—Volume 1年度引用学科排名




书目名称Computational Intelligence in Data Mining—Volume 1读者反馈




书目名称Computational Intelligence in Data Mining—Volume 1读者反馈学科排名

objection

上坡

Pose Invariant Face Recognition for New Born: Machine Learning Approach,ve subjects like newborn, in which the full power of face recognition being a passive biometric technique requires to be implemented and utilized. To handle the large pose variation in newborn, we propose a pose-adaptive similarity method that uses pose-specific classifiers to deal with different co

mosque

Implementation of Data Analytics for MongoDB Using Trigger Utility,ovided by many databases, especially useful in monitoring, rule enforcement, data validation and data analytics etc. MongoDB is non SQL document oriented database. MongoDB is the fastest growing and most demanding non SQL database. Since MongoDB primarily is operated using its out of box tools like

不利

Heart Disease Prediction Using k-Nearest Neighbor Classifier Based on Handwritten Text,the number of deaths occurring due to it and by using handwriting analysis we can achieve this. Handwriting of an individual shows the presence of a heart disease even before physical symptoms appear. The proposed system predicts presence of heart disease based on handwriting analysis using k-Neares

TERRA

TERRA

FICE

Detecting and Analyzing Invariant Groups in Complex Networks, networks has rapidly become a major area in network science. A major limitation of most of the community finding algorithms is the dependence on the ordering in which vertices are processed. However, less study has been conducted on the effect of vertex ordering in community detection. In this pape

Accolade

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