By De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne
This two-volume set LNCS 9225 and LNCS 9226 constitutes - together with the amount LNAI 9227 - the refereed complaints of the eleventh overseas convention on clever Computing, ICIC 2015, held in Fuzhou, China, in August 2015. The 191 complete papers and forty two brief papers of this quantity have been rigorously reviewed and chosen from 191 submissions. The papers are geared up in topical sections equivalent to evolutionary computation and studying; compressed sensing, sparse coding and social computing; neural networks, nature encouraged computing and optimization; trend attractiveness and sign processing; picture processing; biomedical informatics idea and techniques; differential evolution, particle swarm optimization and area of interest know-how; clever computing and data discovery and knowledge mining; tender computing and computer studying; computational biology, protein constitution and serve as prediction; genetic algorithms; synthetic bee colony algorithms; swarm intelligence and optimization; social computing; details protection; digital truth and human-computer interplay; healthcare informatics conception and strategies; unsupervised studying; collective intelligence; clever computing in robotics; clever computing in communique networks; clever keep an eye on and automation; clever info research and prediction; gene expression array research; gene law modeling and research; protein-protein interplay prediction; biology encouraged computing and optimization; research and visualization of enormous organic information units; motif detection; biomarker discovery; modeling; simulation; and optimization of organic platforms; biomedical facts modeling and mining; clever computing in biomedical signal/image research; clever computing in mind imaging; neuroinformatics; cheminformatics; clever computing in computational biology; computational genomics; designated consultation on biomedical info integration and mining within the period of huge facts; designated consultation on mammoth info analytics; certain consultation on synthetic intelligence for ambient assisted dwelling; and designated consultation on swarm intelligence with discrete dynamics.
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Additional resources for Intelligent Computing Theories and Methodologies: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015, Proceedings, Part I
Select the optimal solution, and update the global optimal solution step4. Update each particle radius, ambient temperature and pheromone concentration, and calculate out each particle’s exercise ability step5. Increase the concentration of pheromone on the optimal path, lead the particles move towards the optimal solution; increase the pheromone concentration on random path to implement random wave process in the environment. 5 Experimental Results and Analysis In this section, the VRPSTW problem is solved, and the solution is compared with the results in [10, 11] respectively, according to the comparison and analysis, effectiveness of the proposed algorithm is proved.
6) is selected through searching within the combined set of S1 [ S0 based on a predeﬁned score function OðÁÞ for assessing the compression quality, which will be explained in Sect. 3. Equation (7) drives fWigm i¼1 generated by the multiplicative rule to move towards a pre-selected optimal basis matrix WÃ1 . This design is motivated by a recent evolutionary optimization algorithm inspired by the flashing behavior of ﬁrefly algorithm . It assumes that attractiveness between ﬁreflies is proportional to their brightness, thus, given any two ﬁreflies, one will move towards the other that glows brighter.
MyHadoop-Hadoop-on-Demand on Traditional HPC Resources. San Diego Supercomputer Center Technical report TR-2011–2, University of California, San Diego (2011) 19. : Merging trust in collaborative ﬁltering to alleviate data sparsity and cold start. -Based Syst. 57, 57–68 (2014) 20. : A new similarity function for selecting neighbors for each target item in collaborative ﬁltering. -Based Syst. 37, 146–153 (2013) 21. , Zhang, S. : Collaborative ﬁltering recommendation algorithm based on item clustering and global similarity.