Titlebook: Emerging Intelligent Computing Technology and Applications; 9th International Co De-Shuang Huang,Phalguni Gupta,Michael Gromiha Conference

HAVOC

Niederfrequenzger?te und Signalisierung geometry, MLEN outperforms each of its components and outputs an overall and superior embedding. Experimental results on both synthetic and image manifolds validate the effectiveness of the proposed method.

羊欄

A Novel Feature Selection Technique for SAGE Data Classificationng and testing of two well known classifiers- Extreme Learning Machine (ELM) and Support Vector Machine (SVM). The performance evaluation of ELM and SVM classifiers shows that the proposed feature selection method works well with these classifiers.

decode

A Simple but Robust Complex Disease Classification Method Using Virtual Sample Templateistance. Our experimental results indicate that the proposed method is robust in predicative performance. Compared with other common classification methods of complex disease, our method is simpler and often with improved classification performance.

防御

Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type IIan three previously published differential coexpression analysis (DCEA) methods. We applied the new approach to a public available type 2 diabetes (T2D) expression dataset, and many additional discoveries can be found through our method.

步履蹣跚

aviator

Manifold Learner Ensemble geometry, MLEN outperforms each of its components and outputs an overall and superior embedding. Experimental results on both synthetic and image manifolds validate the effectiveness of the proposed method.

故意

packet

Barter

Multi-objectivization and Surrogate Modelling for Neural Network Hyper-parameters Tuningclassification error of the model. We show the performance of the multi-objectivization approach on five data sets and compare it to a surrogate based single-objective algorithm for the same problem. Moreover, we compare the multi-objectivization approach to two surrogate based approaches – a single-objective one and a multi-objective one.

朋黨派系

An Effective Parameter Estimation Approach for the Inference of Gene Networksptimization techniques are developed to deal with the scalability and network robustness problems, respectively. To validate the proposed approach, experiments have been conducted on several artificial and real datasets. The results show that our approach can be used to infer robust gene networks with desired system behaviors successfully.