Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

Foreknowledge

Time Series Forecasting by Evolving Artificial Neural Networks Using “Shuffle”, Cross-Validation andn the evolutionary process, one called ”shuffle” and another one carried out with cross-validation and ensembles. A study using these two methods will be shown with the aim to evaluate the effect of both methods in the accurateness of the final forecasting.

天然熱噴泉

Prediction of Power Consumption for Small Power Region Using Indexing Approach and Neural Networkower delivery. The paper proposes the novel neural based method of forecasting the power consumption, taking into account the trend of its change associated with the particular hour of the day, type of the day as well as season of the year

配置

Fault Prognosis of Mechanical Components Using On-Line Learning Neural Networkstional data in order to assess its forecasting capabilities. Moreover, the behavior of the method is compared with that of an efficient and fast method, the On-line Sequential Extreme Learning Machine. The accurate predictions of the proposed method pave the way for future development of a complete prognosis system.

鼓掌

perpetual

MILL

lymphoma

urethritis

LAVA

CEDE

Principles of Optical Coherence Tomography,ied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations.