Intelligent Computing Theories and Application
Springer International Publishing (Verlag)
978-3-030-26968-5 (ISBN)
The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is "Advanced Intelligent Computing Methodologies and Applications." Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Particle Swarm Optimization-based Power Allocation Scheme for Secrecy Sum Rate Maximization in NOMA with Cooperative Relaying.- A Discrete Particle Swarm Optimization for PairwiseSequence Alignment.- A Diversity based Competitive Multi-Objective PSO for Feature Selection.- A Decomposition-based Hybrid Estimation of Distribution Algorithm for Practical Mean-CVaR Portfolio Optimization.- CBLNER: a multi-models biomedical named entity recognition system based on machine learning.- Dice Loss in Siamese Network for Visual Object Tracking.- Fuzzy PID Controller for Accurate Power Sharing in DC Microgrid.- Precipitation Modeling and Prediction Based on Fuzzy-control Multi-cellular Gene Expression Programming and Wavelet Transform.- Integrative Enrichment Analysis of Intra- and Inter- Tissues' Differentially Expressed Genes Based on Perceptron.- Identifying differentially expressed genes based on differentially expressed edges.- Gene functional module discovery via integrating geneexpression and PPI network data.- A novel framework for improving the prediction of disease-associated microRNAs.- Precise Prediction of Pathogenic Microorganisms using 16S rRNA Gene Sequences.- Learning from Deep Representations of Multiple Networks for Predicting Drug-Target Interactions.- Simulation of Complex neural firing patterns based on improved deterministic Chay model.- Knowledge based helix angle and residue distance restraint free energy terms of GPCRs.- Improved Spectral Clustering Method for Identifying Cell Types from Single-Cell Data.- A Novel Weight Learning Approach Based on Density for Accurate Prediction of Atherosclerosis.- An effective approach of measuring disease similarities based on the DNN regression model.- Herb Pair Danggui-Baishao:Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach.- End-to-end learning based compound activity prediction using binding pocket information.- A novel approach for predicting lncRNA-disease associations by structural perturbation method.- Improved Inductive Matrix Completion Method for Predicting MicroRNA-Disease Associations.- A link and Weight-Based Ensemble Clustering for Patient Stratification.- HGMDA:HyperGraph for Predicting MiRNA-disease Association.- Discovering Driver Mutation Profiles in Cancer with A Local Centrality Score.- LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction.- Distinguishing driver missense mutations from benign polymorphisms in breast cancer.- A novel method to predict protein regions driving cancer through integration of multi-omics data.- In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information.- Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest.- Prediction of human lncRNAs based on integrated information entropy features.- A Gated Recurrent Unit Model for Drug Repositioning by Combining Comprehensive Similarity Measures and Gaussian Interaction Profile Kernel.- A Novel Approach to predicting miRNA-disease associations.- Hierarchical Attention Network for Predicting DNA-Protein Binding Sites.- Motif discovery via convolutional networks with k-mer embedding.- Whole-Genome Shotgun Sequence of Natronobacterium Gregoryi SP2.- The detection of gene modules with overlapping characteristic via integrating multi-omics data in six cancers.- Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations.- A prediction method of DNA-binding proteins based on evolutionary information.- Research on RNA Secondary Structure Prediction Based on Decision Tree.- Improving hot region prediction by combining gaussian naïve Bayes and DBSCAN.- An efficient LightGBM model to predict protein selfinteracting using Chebyshev moments and bi-gram.- Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-Interacting Proteins.- Identific
| Erscheinungsdatum | 25.07.2019 |
|---|---|
| Reihe/Serie | Information Systems and Applications, incl. Internet/Web, and HCI | Lecture Notes in Computer Science |
| Zusatzinfo | XXI, 790 p. 323 illus., 243 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 1223 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Applications • Artificial Intelligence • Bioinformatics • classification • Clustering • Computer Networks • Computer Science • computer vision • conference proceedings • Cross-validation • decision trees • evolutionary algorithms • Genetic algorithms • Image Processing • Informatics • Learning Algorithms • machine learning • Neural networks • Research • Semantics • Signal Processing • Support Vector Machines • SVM |
| ISBN-10 | 3-030-26968-X / 303026968X |
| ISBN-13 | 978-3-030-26968-5 / 9783030269685 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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