標(biāo)題: Titlebook: Emerging Intelligent Computing Technology and Applications; 9th International Co De-Shuang Huang,Phalguni Gupta,Michael Gromiha Conference [打印本頁(yè)] 作者: 中間時(shí)期 時(shí)間: 2025-3-21 19:31
書目名稱Emerging Intelligent Computing Technology and Applications影響因子(影響力)
書目名稱Emerging Intelligent Computing Technology and Applications影響因子(影響力)學(xué)科排名
書目名稱Emerging Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開度
書目名稱Emerging Intelligent Computing Technology and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Emerging Intelligent Computing Technology and Applications被引頻次
書目名稱Emerging Intelligent Computing Technology and Applications被引頻次學(xué)科排名
書目名稱Emerging Intelligent Computing Technology and Applications年度引用
書目名稱Emerging Intelligent Computing Technology and Applications年度引用學(xué)科排名
書目名稱Emerging Intelligent Computing Technology and Applications讀者反饋
書目名稱Emerging Intelligent Computing Technology and Applications讀者反饋學(xué)科排名
作者: NAG 時(shí)間: 2025-3-21 22:48 作者: Arboreal 時(shí)間: 2025-3-22 01:52
https://doi.org/10.1007/978-3-322-80407-5nential stability of these networks. The derived result improves and extends some related results in the literature. Finally, an illustrative example is provided to demonstrate the effectiveness of our theoretical results.作者: epidermis 時(shí)間: 2025-3-22 05:39
Karl Steinbuch,Werner Rupprechtclassification 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.作者: 易彎曲 時(shí)間: 2025-3-22 12:48 作者: Psa617 時(shí)間: 2025-3-22 14:17 作者: Psa617 時(shí)間: 2025-3-22 19:46
Karl Steinbuch,Werner Rupprechtng 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.作者: Irremediable 時(shí)間: 2025-3-22 21:29 作者: BALE 時(shí)間: 2025-3-23 04:38
,Digitale übertragung im Basisband,an 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.作者: 他很靈活 時(shí)間: 2025-3-23 07:17 作者: HAVOC 時(shí)間: 2025-3-23 10:15
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.作者: 羊欄 時(shí)間: 2025-3-23 14:17
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.作者: decode 時(shí)間: 2025-3-23 20:26
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.作者: 防御 時(shí)間: 2025-3-24 02:01
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.作者: 步履蹣跚 時(shí)間: 2025-3-24 02:40 作者: aviator 時(shí)間: 2025-3-24 08:23
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.作者: 故意 時(shí)間: 2025-3-24 13:52 作者: packet 時(shí)間: 2025-3-24 16:37 作者: Barter 時(shí)間: 2025-3-24 19:45
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.作者: 朋黨派系 時(shí)間: 2025-3-25 03:10
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.作者: 一起平行 時(shí)間: 2025-3-25 03:39 作者: Mindfulness 時(shí)間: 2025-3-25 08:35
https://doi.org/10.1007/978-3-322-94238-8mation content of their common ancestors in the GO hierarchy. A comparative evaluation of our method with other GO-based similarity measures showed that our method outperformed the others in most GO domains.作者: 核心 時(shí)間: 2025-3-25 14:24 作者: collagen 時(shí)間: 2025-3-25 19:17 作者: Pathogen 時(shí)間: 2025-3-25 20:08 作者: exostosis 時(shí)間: 2025-3-26 03:23
https://doi.org/10.1007/978-3-662-33889-6also been used to enhance the searching ability for the global minimum within the. off-lattice model. Experimental results demonstrate that the proposed algorithm has better performance in global optimization and can predict 3D protein structure more effectively.作者: 字謎游戲 時(shí)間: 2025-3-26 07:24
Entwicklungsstand von Nebenstellenanlagen,s. The binding pairs identified from an extensive analysis of protein-DNA complexes and protein-RNA complexes will provide a valuable resource for studying protein-nucleic acid interactions. The database is available at ..作者: Heresy 時(shí)間: 2025-3-26 10:04
Database of Protein-Nucleic Acid Binding Pairs at Atomic and Residue Levelss. The binding pairs identified from an extensive analysis of protein-DNA complexes and protein-RNA complexes will provide a valuable resource for studying protein-nucleic acid interactions. The database is available at ..作者: Decongestant 時(shí)間: 2025-3-26 14:35 作者: fabricate 時(shí)間: 2025-3-26 18:38
Scoring Protein-Protein Interactions Using the Width of Gene Ontology Terms and the Information Contmation content of their common ancestors in the GO hierarchy. A comparative evaluation of our method with other GO-based similarity measures showed that our method outperformed the others in most GO domains.作者: Choreography 時(shí)間: 2025-3-26 21:13
Chinese Sentiment Classification Based on the Sentiment Drop Pointolarity assignment. It predicts the sentiment polarity by a determinative policy which involves two classifiers simultaneously. The experiments show that our approach is efficient and suited for reviews analysis in different domains.作者: FLIRT 時(shí)間: 2025-3-27 03:42
Automated Model Selection and Parameter Estimation of Log-Normal Mixtures via BYY Harmony Learningdemonstrated by the experiments that the proposed BYY harmony learning algorithm not only automatically determines the number of actual log-normal distributions in the sample dataset, but also leads to a satisfactory estimation of the parameters in the original log-normal mixture.作者: FRAX-tool 時(shí)間: 2025-3-27 05:17 作者: Schlemms-Canal 時(shí)間: 2025-3-27 11:31 作者: 我邪惡 時(shí)間: 2025-3-27 16:40 作者: 打火石 時(shí)間: 2025-3-27 19:46 作者: 把…比做 時(shí)間: 2025-3-28 01:21 作者: intrude 時(shí)間: 2025-3-28 02:58 作者: 道學(xué)氣 時(shí)間: 2025-3-28 09:45 作者: Fallibility 時(shí)間: 2025-3-28 12:40
Assessment of Protein-Graph Remodeling via Conformational Graph Entropyer a graph is suitable for representing a protein. The experimental results suggest that when this extended graph entropy is applied, it helps a conformational on protein graph modeling. Besides, it also contributes to the protein structural comparison indirectly if a protein graph is solid.作者: 未完成 時(shí)間: 2025-3-28 15:45
A Novel Feature Selection Technique for SAGE Data Classificationing technique used for measuring the expression levels of genes. Each SAGE library contains expression levels of thousands of genes (or features). It is impossible to consider all these features for classification and also the general feature selection algorithms are not efficient with this data. In作者: 四溢 時(shí)間: 2025-3-28 20:56 作者: Cloudburst 時(shí)間: 2025-3-28 23:19 作者: 暫時(shí)過(guò)來(lái) 時(shí)間: 2025-3-29 05:37
Automated Model Selection and Parameter Estimation of Log-Normal Mixtures via BYY Harmony Learninges, model selection can be made automatically during parameter learning. In this paper, this automated model selection learning mechanism is extended to logarithmic normal (log-normal) mixtures. Actually, an adaptive gradient BYY harmony learning algorithm is proposed for log-normal mixtures. It is 作者: 沒有貧窮 時(shí)間: 2025-3-29 07:48
A Simple but Robust Complex Disease Classification Method Using Virtual Sample Templatege-scale biological data analysis and mining. In this work we propose a simple classification method based on virtual sample template (VST) and three distance measurements. Each VST corresponds to a subclass in training set. The label of a test sample is simply determined by measuring the similarity作者: 令人悲傷 時(shí)間: 2025-3-29 14:31
Biweight Midcorrelation-Based Gene Differential Coexpression Analysis and Its Application to Type IIing Pearson correlation. However, Pearson correlation is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure ‘similarity’ between gene e作者: 傲慢人 時(shí)間: 2025-3-29 18:09
A Hybrid Gene Selection and Classification Approach for Microarray Data Based on Clustering and PSOmicroarray data. In this approach, PSO combining with clustering method are used to perform gene selection to reduce redundancy. Firstly, genes are partitioned into a certain number of clusters by using K-means, and then PSO is used to perform gene selection from the clustered genes. Because of its 作者: Decimate 時(shí)間: 2025-3-29 20:42
Manifold Learner Ensembleccessfully extract intrinsic geometry underlying high-dimensional data cloud. However, there is no work considering the ensemble of local and global manifold learners to promote learning results, where such strategy has achieved great success in classification. In this paper, we propose a manifold l作者: llibretto 時(shí)間: 2025-3-30 00:09 作者: 不整齊 時(shí)間: 2025-3-30 06:10 作者: STING 時(shí)間: 2025-3-30 11:12
An Effective Parameter Estimation Approach for the Inference of Gene Networkscedure of network construction, this work presents an integrated approach for network inference, in which the parameter identification and parameter optimization techniques are developed to deal with the scalability and network robustness problems, respectively. To validate the proposed approach, ex作者: 血統(tǒng) 時(shí)間: 2025-3-30 14:45
1865-0929 gnition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in Computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired 978-3-642-39677-9978-3-642-39678-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 深陷 時(shí)間: 2025-3-30 20:29 作者: 現(xiàn)存 時(shí)間: 2025-3-30 21:31
Emerging Intelligent Computing Technology and Applications9th International Co作者: 通情達(dá)理 時(shí)間: 2025-3-31 02:17 作者: fidelity 時(shí)間: 2025-3-31 09:04 作者: 命令變成大炮 時(shí)間: 2025-3-31 12:38 作者: ICLE 時(shí)間: 2025-3-31 14:28
978-3-642-39677-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE作者: ALE 時(shí)間: 2025-3-31 18:25
Emerging Intelligent Computing Technology and Applications978-3-642-39678-6Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 游行 時(shí)間: 2025-3-31 22:15
Hektor Haark?tter,J?rg-Uwe Nielandvational data, there are still no effective methods for the high dimensional data. In this work, we propose a hybrid approach by taking the advantage of two state of the art causal discovery methods. In the proposed method, the structure learning based methods are explored to discover the causal ske作者: Nonporous 時(shí)間: 2025-4-1 05:23