This special issue comprises of 14 selected papers from the International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE 2011). The conferences received 860 paper submissions from 15 countries and regions, of which 450 were selected for presentation after a rigorous review process. From these 450 research papers, through two rounds of reviewing, the guest editors selected 14 as the best papers on the Computers track of the Conference. The candidates of the Special Issue are all the authors, whose papers have been accepted and presented at the CSEEE 2011, with the contents not been published elsewhere before.
   2011 International Workshop on Computer Science for Environmental Engineering and EcoInformatics will continue the excellent tradition of gathering world-class scientists, engineers and educators engaged in the fields of Computer Science and Environmental Biotechnology to meet and present their latest activities. CSEEE 2011 held on July 29-31, 2011, Kunming, China. This conference is sponsored by International Association for Scientific and High Technology, and is in cooperation with Yunnan University, and it is technical co-sponsored by Kunming University of Science and Technology.
   “A Novel Weighted Voting for K-Nearest Neighbor Rule”, by Jianping Gou, Taisong Xiong and Yin Kuang, mainly focuses on the outstanding issue about the sensitivity of the selection of the neighborhood size k for k-nearest neighbor rule (KNN) in pattern classification. Experimental results show that the proposed classifier is an effective algorithm because of its better classification performance and more robustness over a wide range of k.
  “Differential Artificial Bee Colony Algorithm for Global Numerical Optimization”, by Bin Wu and Cunhua Qian, proposes a modified version of the ABC, which has been embedded to enhance the global searching capability by differential operator. Simulation results show that the DABC can effectively enhance the searching efficiency and greatly improve the searching quality.
   “Detecting Algorithm for Moving Objects Based on Bayesian Judging Criterion”, by Yingxia Liu and Faliang Chang, considers the problem of accuracy for judging threshold under the complicated circumstance. In order to adapt the change of the light, Bayesian judging criterion is used to detect object, void warm price and falling report price are considered comprehensively, combined with likelihood function and Bayesian risk assessment, an adaptive threshold which determined by mean and variance of every image is obtained. The experimental result shows that the background noise can be removed with the optimal threshold and the moving objects can be detected accurately.