Titlebook: Intelligent Computing; Proceedings of the 2 Kohei Arai,Supriya Kapoor,Rahul Bhatia Conference proceedings 2019 Springer Nature Switzerland

別名

考博

Extreme Learning Machines in Predicting the Velocity Distribution in Compound Narrow Channels,are employed to model the velocity field. The results indicate that ELM performed adequately (MAPE?=?3.03; RMSE?=?0.03) in velocity field prediction. However, it was found that the ELM offers higher performance than the existing methods.

CLEAR

Rice Classification Using Scale Conjugate Gradient (SCG) Backpropagation Model and Inception V3 Mod Kernel 1121, Steam 86, Basmati, super, Steam 85 and Super Tota. Each class contains 120 samples for training. Total 18 samples of rice are used for testing the network accuracy of each dataset. In this research, data is classified based on maximum area of the grain. The results reveal that Inceptio

我不怕犧牲

,A Machine Learning Approach to Analyze and Reduce Features to a Significant Number for Employee’s Tch can be considered as the foremost features that lead to employee turnover. Our two steps feature selection technique confirms that there are mainly three features that are responsible for employee’s departure. Later, these selected minimal features have been tested with state of the art algorithm

VICT

Legal Document Retrieval Using Document Vector Embeddings and Deep Learning,ng legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semanti

擁護

Recognition of Heart Murmur Based on Machine Learning and Visual Based Analysis of Phonocardiographd interpreter dependent visual analysis). We used machine learning based on mel frequency cepestral coefficients as a feature and Hidden Markov Model (HMM) as a classifier. We performed visual analysis based on phonocardiography (PCG) and spectrogram image. Model A ACCA demonstrated 97% CCR, 99.2% s

flimsy

ULCER

分發(fā)

TAP