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Titlebook: Bio-Inspired Computing and Applications; 7th International Co De-Shuang Huang,Yong Gan,Kyungsook Han Conference proceedings 2012 Springer-V

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發(fā)表于 2025-3-21 19:16:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bio-Inspired Computing and Applications
期刊簡(jiǎn)稱7th International Co
影響因子2023De-Shuang Huang,Yong Gan,Kyungsook Han
視頻videohttp://file.papertrans.cn/187/186339/186339.mp4
發(fā)行地址Up-to-date results.Fast track conference proceedings.State-of-the-art report
學(xué)科分類Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Bio-Inspired Computing and Applications; 7th International Co De-Shuang Huang,Yong Gan,Kyungsook Han Conference proceedings 2012 Springer-V
影響因子The three-volume set LNCS 6838, LNAI 6839, and LNBI 6840 constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Intelligent Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. This volume contains 93 revised full papers, from a total of 281 presentations at the conference - carefully reviewed and selected from 832 initial submissions. The papers address all issues in Advanced Intelligent Computing, especially Methodologies and Applications, including theories, methodologies, and applications in science and technology. They include a range of techniques such as artificial intelligence, pattern recognition, evolutionary computing, informatics theories and applications, computational neuroscience and bioscience, soft computing, human computer interface issues, etc.
Pindex Conference proceedings 2012
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發(fā)表于 2025-3-21 20:30:56 | 只看該作者
Gehalte, Emolumente. Brandversicherung,is developed to predict the short term stock trend. Finally, integration between these two modules is established using neural network. The System was trained and tested with real data from the Egyptian stock market. The obtained results are encouraging.
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https://doi.org/10.1007/978-3-642-94228-0odel. Finally, to identify a compact GAM for a given center gene, a heuristic search procedure is designed. Experimental results on three publicly available gene expression data sets show the effectiveness and efficiency of the proposed kGAM model in identifying cancer-related gene association patterns.
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發(fā)表于 2025-3-22 13:20:17 | 只看該作者
A Class of Chaotic Neural Network with Morlet Wavelet Function Self-feedbackd and is used to instruct the parameter set of the networks for solving traveling salesman problem (TSP). Simulation researches on 10-city TSP indicate that the proposed networks can find the optimal solutions of combinatorial optimization problems.
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發(fā)表于 2025-3-22 20:49:33 | 只看該作者
Stock Market Trend Prediction Model for the Egyptian Stock Market Using Neural Networks and Fuzzy Lois developed to predict the short term stock trend. Finally, integration between these two modules is established using neural network. The System was trained and tested with real data from the Egyptian stock market. The obtained results are encouraging.
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發(fā)表于 2025-3-22 21:46:06 | 只看該作者
Inferring Protein-Protein Interactions Based on Sequences and Interologs in Mycobacterium Tuberculoscies were mapped to the proteome of . by the interolog method. The ensemble protein interactions were then validated by various functional linkages i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources.
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發(fā)表于 2025-3-23 03:21:43 | 只看該作者
Predicting Long-Term Vaccine Efficacy against Metastases Using Agents two protocols by delaying the time of the first administration, in order to catch up the maximum time delay between the appearing of metastases and the administration of the vaccine needed to guarantee reasonable treatment efficacy.
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發(fā)表于 2025-3-23 09:33:07 | 只看該作者
A New Method for Identifying Cancer-Related Gene Association Patternsodel. Finally, to identify a compact GAM for a given center gene, a heuristic search procedure is designed. Experimental results on three publicly available gene expression data sets show the effectiveness and efficiency of the proposed kGAM model in identifying cancer-related gene association patterns.
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