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MARC Lista (tabellare)
Advanced Intelligent Computing [[electronic resource] ] : 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers / / edited by De-Shuang Huang, Yong Gan, Vitoantonio Bevilacqua, Juan Carlos Figueroa
Advanced Intelligent Computing [[electronic resource] ] : 7th International Conference, ICIC 2011, Zhengzhou, China, August 11-14, 2011. Revised Selected Papers / / edited by De-Shuang Huang, Yong Gan, Vitoantonio Bevilacqua, Juan Carlos Figueroa
Edizione [1st ed. 2012.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Descrizione fisica 1 online resource (XXI, 707 p.)
Disciplina 006.3
Collana Theoretical Computer Science and General Issues
Soggetto topico Artificial intelligence
Pattern recognition systems
Application software
Computer vision
Computer science
User interfaces (Computer systems)
Human-computer interaction
Artificial Intelligence
Automated Pattern Recognition
Computer and Information Systems Applications
Computer Vision
Theory of Computation
User Interfaces and Human Computer Interaction
ISBN 3-642-24728-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996465938403316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Yijie Pan, Qinhu Zhang
Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Yijie Pan, Qinhu Zhang
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (505 pages)
Disciplina 572.80285
Collana Lecture Notes in Bioinformatics
Soggetto topico Computational intelligence
Artificial intelligence
Bioinformatics
Computational Intelligence
Artificial Intelligence
Computational and Systems Biology
ISBN 981-9756-92-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Biomedical Data Modeling and Mining -- AAHLDMA: Predicting Drug-Microbe Associations Based on Bridge Graph Learning -- 1 Introduction -- 2 Materials -- 2.1 Drug Similarity Attribute -- 2.2 Drug Network Topological Attribute -- 2.3 Fused Drug Attribute -- 2.4 Microbe Functional Similarity Attribute -- 2.5 Microbe Sequence Attribute -- 2.6 Fused Microbe Attribute -- 3 Methods -- 3.1 Attention-Based Graph Autoencoder -- 3.2 Construction of Bridge Graph -- 3.3 Classification -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Model Performance -- 4.3 Performance Comparison with Other Models -- 4.4 Case Study -- 5 Conclusion -- References -- Adaptive Weight Sampling and Graph Transformer Neural Network Framework for Cell Type Annotation of Scrna-seq Data -- 1 Introduction -- 2 Materials -- 2.1 scRNA-seq Datasets -- 2.2 Gene Interaction Networks -- 3 Methods -- 3.1 Adaptive Sampling -- 3.2 Graph Representation Module -- 4 Experimental Results -- 4.1 Model ACC Performance -- 4.2 Model ACC Performance -- 4.3 Sankey Diagram Representation of the Model on the Data Set -- 5 Conclusion -- References -- BiLETCR: An Efficient PMHC-TCR Combined Forecasting Method -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 BiLETCR Model Structure and Forecasting Process -- 3.2 EMA -- 3.3 Model Training -- 4 Experiments -- 4.1 Data Collection and Processing -- 4.2 Experimental Design -- 4.3 Adding EMA Module Can Improve the Performance of the Model -- 4.4 For the Generalization Test of BiLETCR, the Prediction Effect of BiLETCR is Better Than the Existing Model -- 4.5 BiLETCR is Superior to the Existing Model in Computational Efficiency -- 4.6 BiLETCR is Superior to the Existing Prediction Tools on Ts-Special Test Set -- 5 Conclusion -- References.
CDDTR: Cross-Domain Autoencoders for Predicting Cell Type Specific Drug-Induced Transcriptional Responses -- 1 Introduction -- 2 Materials and Methods -- 2.1 Within-Domain Reconstruction Paths -- 2.2 Cross-Domain Reconstruction Paths -- 2.3 Training and Prediction Procedures -- 2.4 Comparison with Alternative Methods -- 3 Results -- 3.1 Comparison Results with the State-of-the-Art Methods -- 3.2 The Performance of CDDTR on Small Sample Data -- 3.3 Biological Interpretability of CDDTR Model -- 3.4 Further Improvement of Prediction Performance of CDDTR -- 3.5 Case Study -- 4 Conclusion and Discussion -- References -- ChiMamba: Predicting Chromatin Interactions Based on Mamba -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets and Processing -- 2.2 Selective State Space Models -- 2.3 ChiMamba Model -- 3 Experiment -- 3.1 Datasets and Experiment Setup -- 3.2 Comparative Studies -- 3.3 Ablation Studies -- 3.4 Training Time -- 4 Discussion -- References -- Cluster Analysis of Scrna-Seq Data Combining Bioinformatics with Graph Attention Autoencoders and Ensemble Clustering -- 1 Introduction -- 2 Materials -- 2.1 Dataset -- 2.2 Processing Gene Expression Matrix -- 2.3 Denoising Using Network Enhancement -- 2.4 Performing Principal Component Analysis -- 3 Methods -- 3.1 Graph Attention Autoencoder -- 3.2 Bipartite Graph Ensemble Clustering Method -- 4 Experimental Results -- 4.1 Model Performance -- 4.2 Comparison of Different Model -- 4.3 Comparison of Different Datasets -- 5 Conclusion -- References -- Compound-Protein Interaction Prediction with Sparse Perturbation-Aware Attention -- 1 Introduction -- 2 Methodology -- 2.1 Prediction Backbone -- 2.2 Perturbation-Aware Attention -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Implementation Details -- 3.3 Comparative Performance -- 3.4 Impacts of Modules and Parameters -- 3.5 Case Study.
4 Related Work -- 5 Conclusion -- References -- CUK-Band: A CUDA-Based Multiple Genomic Sequence Alignment on GPU -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Strategy of Affine Gap Penalty and K-band -- 3.2 Improved Central Star Strategy Based on Bitmap -- 3.3 The K-band Strategy Based on CUDA -- 4 Performance Evaluation -- 4.1 Datasets and Evaluation Criterion -- 4.2 Configuration -- 4.3 Results -- 5 Conclusion -- References -- DeepMHAttGRU-DTI: Prediction of Drug-Target Interactions Based on Knowledge Graph Random Walk Embeddings and GRU Neural Network -- 1 Introduction -- 2 Materials and Methods -- 2.1 Graph Embedding Algorithm Based on Three Improved Random Walk Algorithms -- 2.2 GRU Binary Classification Neural Network Model -- 2.3 Multi-head Attention -- 3 Experimental Results -- 3.1 Evaluation Criteria -- 3.2 General Dataset -- 3.3 Comparison of Different Random Walk Algorithms Using GRU Model -- 3.4 Comparison Between the GRU Model and the MHAttGRU Model -- 3.5 Comparison with Other Existing Models -- 4 Conclusion -- References -- DiagNCF: Diagnosis Neural Collaborative Filtering for Accurate Medical Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Setup and Notation -- 2.2 Data-Preprocessing -- 3 Diagnose Neural Collaborative Filtering (DiagNCF) -- 3.1 General Framework -- 3.2 Generalized Matrix Factorization (GMF) -- 3.3 Multi-Layer Perception (MLP) -- 3.4 DiagNCF -- 4 Experiments -- 4.1 Performance Evaluation -- 4.2 Training Procedure -- 5 Conclusions -- References -- Drug Molecule Generation Method Based on Fusion of Protein Sequence Features -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Targeted Drug Generation Process -- 3 Experimental Results -- 3.1 Evaluation of Model Performance -- 3.2 Molecular Docking Results -- 4 Conclusion -- References.
Drug Target Affinity Prediction Based on Graph Structural Enhancement and Multi-scale Topological Feature Fusion -- 1 Introduction -- 2 Methods -- 2.1 Model Architecture -- 2.2 Drug Feature Extraction Module -- 2.3 Protein Feature Extraction Module -- 2.4 Multi-scale Topological Feature Fusion Module -- 3 Results and Discussion -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Parameters Setting -- 3.4 Performance Comparison with Baseline Model -- 4 Ablation Experiment -- 5 Conclusion -- References -- Drug-Target Interaction Prediction Based on Multi-path Graph Convolution and Graph-Level Attention Mechanism -- 1 Introduction -- 2 Methods -- 2.1 Method Overview -- 2.2 Feature Extraction -- 2.3 Multi-feature Graph Convolution Module -- 2.4 Loss Function -- 3 Results -- 3.1 Dataset -- 3.2 Experiment Settings -- 3.3 Comparisons with Other Baseline Methods -- 3.4 Ablation Experiments -- 3.5 Model Generalization Test -- 4 Conclusion -- References -- Fully Convolutional Neural Network for Predicting Cancer-Specific CircRNA-MiRNA Interaction Sites -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data -- 2.2 Modelling -- 3 Results -- 4 Discussion and Conclusion -- References -- GSDPI: An Integrated Feature Extraction Framework for Predicting Novel Drug-Protein Interaction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Low-Dimensional Feature Vectors and Feature Similarity Matrices -- 2.3 Determining the Dimensionality of Feature Matrix -- 2.4 Calculation of the Feature Similarity Matrices -- 2.5 DPI Prediction Model Based on GSDPI -- 3 Experimental Evaluation -- 3.1 Evaluation Metrics -- 3.2 Method Comparison and Parameter Settings -- 3.3 Experimental Comparison -- 3.4 Ablation Experiments -- 3.5 Integrate the Gene Ontology (GO) Annotation for All Drug Target-Coding Genes -- 3.6 Case Study -- 4 Conclusion -- References.
Heterogeneous Genome Compression on Mobile Devices -- 1 Introduction -- 2 Related Works -- 2.1 Genome Data Compression -- 2.2 Hardware Accelerated Bioinformatics -- 3 Background -- 3.1 Heterogeneity of MPSoC -- 3.2 Dynamic Voltage-Frequency Scaling -- 4 Methods -- 4.1 Distribute Tasks Transparently -- 4.2 Pipeline Organization -- 5 Results and Discussions -- 5.1 Test Data -- 5.2 Performance and Energy Efficiency Improvements of Heterogeneous Gzip -- 5.3 Exploration for the Reason of Extra Energy Consumption and Discussion -- 6 Conclusion -- References -- HyperCPI: A Novel Method Based on Hypergraph for Compound Protein Interaction Prediction with Good Generalization Ability -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Hypergraphs -- 2.3 Model Architecture of HyperCPI -- 3 Results and Discussion -- 3.1 Performance on OOD Experiments -- 3.2 Ablation Study -- 4 Conclusion -- References -- iEMNN: An Iterative Integration Method for Single-Cell Transcriptomic Data Based on Network Similarity Enhancement and Mutual Nearest Neighbors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Overview of iEMNN -- 2.2 Network Similarity Enhancement -- 2.3 Methods for Comparison -- 2.4 Performance Metrics -- 3 Results -- 3.1 iEMNN Enhances the Similarity of Similar Cells While Separating Distinct Cells -- 3.2 Scenario 1: iEMNN in the Scenario of Identical Cell Types -- 3.3 Scenario 2: iEMNN in the Scenario of Non-identical Cell Types -- 3.4 Scenario 3: iEMNN in the Scenario of Multiple Batches -- 3.5 Scenario 4: iEMNN in the Scenario of Cross-Species -- 4 Discussion -- References -- IGDACA: Imaging Genomics of Deep Autoencoder Cascade Attention Fusion Networks for Cervical Cancer Prognosis Prediction -- 1 Introduction -- 2 Method -- 2.1 Model Design -- 2.2 Image Feature Extraction -- 2.3 Gene Feature Extraction -- 2.4 Attention Fusion Module.
3 Experiments and Analyses.
Record Nr. UNINA-9910878052503321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Qinhu Zhang, Jiayang Guo
Advanced Intelligent Computing in Bioinformatics : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Qinhu Zhang, Jiayang Guo
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (490 pages)
Disciplina 572.80285
Collana Lecture Notes in Bioinformatics
Soggetto topico Computational intelligence
Artificial intelligence
Bioinformatics
Computational Intelligence
Artificial Intelligence
Computational and Systems Biology
ISBN 981-9756-89-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Biomedical Data Modeling and Mining -- Alzheimer's Disease Diagnosis via Specific-Shared Representation Learning in Multimodal Neuroimaging -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Shallow Feature Learning -- 2.3 Modality Specific Representation Learning -- 2.4 Modality Shared Representation Learning -- 2.5 Modality Specific-Shared Representation Learning -- 3 Experiments -- 3.1 Materials and Image Pre-processing -- 3.2 Comparison Methods -- 3.3 Experimental Setup -- 3.4 Evaluation of Automated Diseases Diagnosis -- 3.5 Ablation Study -- 4 Conclusion -- References -- An Activity Graph-Based Deep Convolutional Neural Network Framework in Symptom Severity Diagnosis Towards Parkinson's Disease Using Inertial Sensors -- 1 Introduction -- 2 Subjects and Data Collection -- 2.1 Participants -- 2.2 Data Collection -- 3 Methodology -- 3.1 Activity Graph Generation -- 3.2 Data Augmentation -- 3.3 Convolutional Neural Network -- 4 Results -- 5 Discussion and Conclusion -- References -- An Optimization Method for Drug Design Based on Molecular Features -- 1 Introduction -- 2 Methods -- 2.1 Pocket of Targeted Protein -- 2.2 Feature Extraction of Targeted Protein -- 2.3 Feature Representation of Drug Molecule -- 2.4 Model -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Comparison of Experiments -- 4 Conclusion -- References -- Application of Machine Learning and Large Language Model Module for Analyzing Gut Microbiota Data -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Machine Learning Algorithms -- 2.3 Chat2GM - a LLM Module Based on Langchain Framework -- 3 Applications and Analysis -- 3.1 Data -- 3.2 Species Diversity Analysis with Statistical Methods -- 3.3 Identification of Obesity-Related Biomarkers via Machine Learning.
3.4 Gut Microbiota Data Analysis with Chat2GM Module -- 4 Conclusions -- References -- CVAE-Based Hybrid Sampling Data Augmentation Method and Interpretation for Imbalanced Classification of Gout Disease -- 1 Introduction -- 2 Materials and Methods -- 2.1 CVAE-Based Hybrid Sampling -- 2.2 Detection Model -- 2.3 Interpretation -- 3 Experiment and Result -- 3.1 Datasets -- 3.2 Classification Results -- 3.3 Comparison of Balancing Strategies -- 3.4 Model Interpretation -- 4 Conclusion -- References -- DepthParkNet: A 3D Convolutional Neural Network with Depth-Aware Coordinate Attention for PET-Based Parkinson's Disease Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Depth-Aware Coordinate Attention -- 2.2 PDaug -- 2.3 Class-Balanced Loss -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Implementation Details -- 3.3 Comparison -- 3.4 Ablation Study -- 4 Conclusion -- References -- Gene Selection and Classification Method Based on SNR and Multi-loops BPSO -- 1 Introduction -- 2 Method -- 2.1 The Multi-loops BPSO -- 3 Experiments and Results -- 3.1 Experiment Preparation -- 3.2 Experimental Design Principles -- 3.3 Preprocessing by SNR -- 3.4 The Comparison of One-Loop and Multi-loops on BPSO -- 3.5 Comparative Experiment and Analysis -- 4 Conclusion -- References -- Graph Convolutional Networks Based Multi-modal Data Integration for Breast Cancer Survival Prediction -- 1 Introduction -- 2 Method -- 2.1 Feature Selection and Fusion -- 2.2 Patient-Patient Graph Construction -- 2.3 Multi-modal Graph Convolutional Networks Module -- 2.4 Training Details -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Comparisons with State-of-The-Art -- 3.3 Ablation Studies -- 3.4 Validation -- 4 Conclusion and Future Work -- References -- IDHPre: Intradialytic Hypotension Prediction Model Based on Fully Observed Features -- 1 Introduction.
2 Related Work -- 2.1 Imputation of Missing Values -- 2.2 Feature Selection -- 3 IDHPre -- 3.1 Imputation of Missing Values -- 3.2 Feature Selection -- 4 Experiment and Evaluation -- 4.1 Implementation Details -- 4.2 Qualitative and Quantitative Comparison -- 4.3 Ablation Study -- 5 Conclusion -- References -- Machine Learning Models for Improved Cell Screening -- 1 Introduction -- 2 Related Work -- 2.1 Mainstream Cell Line Screening Methods -- 2.2 Model Stacking -- 3 Dataset -- 4 Proposed Methods -- 4.1 Stacked Machine Learning Method (SMLM) -- 4.2 Simple Linear Method (SLM) -- 4.3 Model Characteristics and Applicability Analysis -- 5 Experimental Results -- 5.1 Experimental Setup -- 5.2 Experimental Analysis -- 6 Conclusion and Pen Question -- References -- Prediction of Bladder Cancer Prognosis by Deep Cox Proportional Hazards Model Based on Adversarial Autoencoder -- 1 Introduction -- 2 Methods -- 2.1 The Framework of the Study -- 2.2 Adversarial Autoencoders -- 2.3 The Architecture of AAE-Cox -- 3 Results -- 3.1 Datasets -- 3.2 Experiments -- 3.3 Evaluations of Cancer Outcomes Prediction -- 3.4 Method Comparison -- 3.5 Independent Test -- 3.6 Identification of Cancer-Related Prognostic Markers and Pathways -- 4 Conclusion and Discussion -- References -- SGEGCAE: A Sparse Gating Enhanced Graph Convolutional Autoencoder for Multi-omics Data Integration and Classification -- 1 Introduction -- 2 Methods -- 2.1 Overview of SGEGCAE -- 2.2 AE for Attribute Information Representation -- 2.3 EGCAE for Feature Representations -- 2.4 Sparse Gating Strategy for Enhanced Feature Representations -- 2.5 TCP for Omics Informativeness Estimation -- 2.6 TFN for Multi-omics Integration -- 3 Experiments and Results -- 3.1 Datasets and Evaluation Metrics -- 3.2 Analysis of Classification Results -- 3.3 Ablation Studies -- 3.4 Analysis of Hyper-parameter.
3.5 Analysis of Different Omics Data Types -- 4 Conclusion -- References -- Short-Term Blood Glucose Prediction Method Based on Signal Decomposition and Bidirectional Networks -- 1 Introduction -- 2 Short-Term Blood Glucose Prediction Method Based on Signal Decomposition and Bidirectional Networks -- 2.1 Overall Approach -- 2.2 Variation Mode Decomposition Algorithm Based on Sparrow Search -- 2.3 Composite Network of Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) -- 3 Results and Analysis -- 3.1 Experimental Environment and Parameter Settings -- 3.2 Model Performance Evaluation Metrics -- 3.3 Model Performance Evaluation Metrics -- 4 Conclusion -- References -- SLGNNCT: Synthetic Lethality Prediction Based on Knowledge Graph for Different Cancers Types -- 1 Introduction -- 2 Dataset -- 3 Method -- 3.1 Knowledge Graph Level Gene Embedding Generation -- 3.2 Message Aggregation Based on Factors -- 3.3 Calculation of Synthetic Lethal Interaction Probabilities -- 4 Experiment -- 4.1 Baselines -- 4.2 Model Evaluation -- 4.3 Results and Analysis of Ablation Experiments -- 5 Conclusion -- References -- TransPBMIL: Transformer-Based Weakly Supervised Prognostic Prediction in Ovarian Cancer with Pseudo-Bag Strategy -- 1 Introduction -- 2 Materials and Methods -- 2.1 Participants and Dataset Generation -- 2.2 TransPBMIL Framework -- 3 Result -- 3.1 Comparison with Existing Weakly Supervised Works -- 3.2 The Performance Improvement Brought by the Pseudo-Bag Strategy. -- 3.3 Visualization of Detection Results -- 4 Conclusion -- References -- Biomedical Informatics Theory and Methods -- A Heterogeneous Cross Contrastive Learning Method for Drug-Target Interaction Prediction -- 1 Introduction -- 2 Method -- 2.1 Graph Embedding Module -- 2.2 Self-contrast Module -- 2.3 Cross-Contrast Module.
2.4 Pairwise Judgment Module -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Experimental Results. -- 3.4 Parameter Sensitivity Analysis. -- 4 Conclusion -- References -- A Retrieval-Based Molecular Style Transformation Optimization Model -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Molecular Retriever -- 2.3 Information Fusion Module and Decoder -- 2.4 Retrieval-Based Molecular Style Transformation Generative Network -- 3 Results -- 3.1 Datasets and Performance Metrics -- 3.2 Results on the QED and PlogP Tasks -- 3.3 Ablation Experiments -- 3.4 Visualized Optimization Results -- 3.5 Parameter Analysis -- 4 Conclusion -- References -- Aggregation Strategy with Gradient Projection for Federated Learning in Diagnosis -- 1 Introduction -- 2 Method -- 2.1 Problem Definition -- 2.2 Federal Projection Matrix -- 2.3 Local Training with GPM -- 3 Experiment -- 3.1 Datasets and Experiment Settings -- 3.2 Implementation Details -- 3.3 Evaluation and Discussion -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Coronary Artery 3D/2D Registration Based on Particle Swarm Optimization of Contextual Morphological Features -- 1 Introduction -- 2 Proposed Method -- 2.1 DSA Vessel Intersection Extraction -- 2.2 CTA Vessel Intersection Extraction -- 2.3 3D-2D Vessel Matching Based on PSO -- 3 Experiments and Results -- 3.1 DSA Vessel Intersection Extraction Results -- 3.2 Results of CTA Vascular Center Line and Intersection -- 3.3 Results of Vascular Matching Between CTA and DSA -- 4 Conclusions -- References -- Enhancing Drug-Drug Interaction Predictions in Biomedical Knowledge Graphs Through Integration of Householder Projections and Capsule Network Techniques -- 1 Introduction -- 2 Preliminaries -- 2.1 Projective Space -- 2.2 Advanced Formulation of Householder Projections -- 3 Model -- 3.1 Relational Householder Projections.
3.2 Möbius Representation Transformation.
Record Nr. UNINA-9910878049003321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Xiankun Zhang, Wei Chen
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part I / / edited by De-Shuang Huang, Xiankun Zhang, Wei Chen
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (XIX, 525 p. 186 illus., 140 illus. in color.)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Computational intelligence
Computer networks
Machine learning
Application software
Computational Intelligence
Computer Communication Networks
Machine Learning
Computer and Information Systems Applications
ISBN 9789819755783
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910881091903321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part IV / / edited by De-Shuang Huang, Chuanlei Zhang, Wei Chen
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part IV / / edited by De-Shuang Huang, Chuanlei Zhang, Wei Chen
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (516 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Computational intelligence
Computer networks
Machine learning
Application software
Computational Intelligence
Computer Communication Networks
Machine Learning
Computer and Information Systems Applications
ISBN 981-9755-91-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part IV -- Neural Networks -- Multi-mode Graph Attention-Based Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The MulGAT Approach -- 3.1 Network Encoder -- 3.2 Structure and Attribute Decoder -- 3.3 Anomaly Detection -- 3.4 Experimental Setup -- 3.5 Results Analysis -- 3.6 Parameter Analysis -- 3.7 Ablation Study -- 4 Conclusions -- References -- A Hierarchical Multi-scale Cortical Learning Algorithm for Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 2.1 CLA -- 2.2 Multi-scale Method -- 3 Hierarchical Multi-scale CLA -- 3.1 Hierarchical Multi-scale CLA Structure -- 3.2 Multi-scale Temporal Context Disentanglement -- 3.3 Adaptive Decoder for Prediction Aggregation -- 4 Experiment -- 4.1 Datasets -- 4.2 Comparison Algorithms -- 4.3 Evaluation Metric -- 4.4 Experimental Parameters -- 4.5 Time Series Forecasting -- 4.6 Epoch Evaluation -- 4.7 Comparison of Column Number Training -- 5 Conclusion -- References -- Knowledge Tracing with Contrastive Learning and Attention-Based Long Short-Term Memory Network -- 1 Introduction -- 2 Model -- 2.1 Problem Formulation -- 2.2 Embedding -- 2.3 ALSTM -- 2.4 Prediction -- 2.5 CL Framework -- 3 Experiments and Analysis -- 3.1 Datasets -- 3.2 Baselines -- 3.3 Evaluation Metrics and Parameter Settings -- 3.4 Comparison of Performance (RQ1) -- 3.5 Comparison on Augmentation Methods (RQ2) -- 3.6 Influence of Model Components (RQ3) -- 3.7 Case Study -- 4 Conclusion -- References -- Multi-label Classification for Concrete Defects Based on EfficientNetV2 -- 1 Introduction -- 2 Related Work -- 2.1 Attention Mechanism -- 3 Methodology -- 3.1 Spatial and Channel Reconstruction Attention Module -- 3.2 Spatial Pyramid Pooling-Fast -- 3.3 Asymmetric Loss -- 4 Experimental Study -- 4.1 Dataset -- 4.2 Evaluation Metrics.
4.3 Experiment Results and Analysis -- 5 Conclusion -- References -- 3D Convolution Channel Compression for Stereo Matching -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Analysis of the Cost Aggregation Module -- 3.2 3D Convolution Channel Compression for Stereo Matching -- 3.3 Analysis of Computational Efficiency -- 4 Experiments and Results -- 4.1 Datasets and Evaluation Metrics -- 4.2 Experimental Setup -- 4.3 Channel Compression for PSMNet -- 4.4 Channel Compression for GwcNet -- 4.5 Channel Compression for CFNet -- 4.6 KITTI Benchmark -- 5 Conclusion -- References -- Designing Real-Time Neural Networks by Efficient Neural Architecture Search -- 1 Introduction -- 2 Related Work -- 2.1 Neural Network Design -- 2.2 Time Analysis of Neural Networks -- 3 Problem Formulation -- 4 RetNAS Framework -- 4.1 EVT-Based WCET Estimator -- 4.2 Constraint Schedule -- 4.3 Maximum Entropy-Based Search -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Evaluation for WCET Estimator -- 5.3 Comparison with Hand-Crafted Neural Architecture -- 5.4 Comparative Analysis with SOTA NAS Methods -- 6 Conclusion -- References -- Uncertainty-Driven Multi-scale Feature Fusion Network for Real-Time Image Deraining -- 1 Introduction -- 2 Uncertainty-Driven Multi-scale Feature Fusion Network -- 2.1 Encoder-Decoder Deraining Network -- 2.2 Uncertainty Estimation -- 2.3 Loss Function -- 3 Experimental -- 3.1 Experimental Settings -- 3.2 Experimental Settings -- 3.3 Ablation Study -- 3.4 Computational Costs -- 4 Conclusion -- References -- RAY-Net: A Motorcycle Helmet Detection Method Integrated Auxiliary Correction -- 1 Introduction -- 2 Related Works -- 3 Datasets -- 3.1 Open-Source Datasets -- 3.2 EMHDD -- 4 Methodology -- 4.1 RAY-Net -- 4.2 Detector -- 4.3 Recognizer -- 5 Experiments -- 5.1 Settings -- 5.2 Comparison Experiments -- 5.3 Ablation Experiments.
6 Conclusions -- References -- Augmented Fuzzy Min-Max Neural Network Driven to Preprocessing Techniques and Space Search Optimization Algorithm -- 1 Introduction -- 2 Preliminaries and Motivation -- 2.1 Fuzzy Min-Max Neural Network -- 2.2 Motivation of AFMNN -- 3 Architecture of AFMNN -- 3.1 Preprocessing Part by Using Information Gain (IG) -- 3.2 Hyperbox Generation Part -- 4 Design of AFMNN -- 5 Experimental Studies -- 5.1 Experiment I -- 5.2 Experiment II -- 5.3 Experiment III -- 5.4 Experiment IV -- 6 Conclusion -- References -- Building Change Detection Based on Fully Convolutional Network in High-Resolution Remote Sensing Images -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overall Structure -- 3.2 Feature Difference Enhancement (FDE) Module -- 3.3 Boundary Feature Compensation (BFC) Module -- 3.4 Multi-Scale Feature Aggregation (MSFA) Module -- 3.5 Change Guide Module (CGM) -- 3.6 Loss Function -- 4 Experimental and Analysis -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Comparative Experiment -- 4.4 Ablation Experiments -- 5 Conclusion -- References -- Optimization of NUMA Aware DNN Computing System -- 1 Introduction -- 2 Related Work -- 2.1 Universal NUMA Optimization Technology -- 2.2 Framework Level NUMA Optimization Technology -- 3 DNN Computing Memory Access Latency Model and Optimization Ideas for NUMA Awareness -- 3.1 NUMA Memory Access Latency Model for DNN Computing -- 3.2 Optimization Problem Based on Latency Models -- 3.3 Optimization Ideas for NUMA Aware DNN Computing -- 4 Design of NUMA Aware DNN Computing System -- 4.1 Optimization of Memory Access Patterns Consistency -- 4.2 Batch-Level Parallel Task Allocation -- 4.3 Page-Aligned Memory Allocation and NUMA Aware Memory Pool -- 5 Experiments and Evaluation -- 5.1 Overall Evaluation -- 5.2 Evaluation of Each Layer -- 5.3 Evaluation of Scalability -- 6 Conclusion.
References -- Pest-YOLO: A Lightweight Pest Detection Model Based on Multi-level Feature Fusion -- 1 Introduction -- 2 Data Augmentation -- 3 Method -- 3.1 Pest-YOLO Model Architecture -- 3.2 Ghost-CF Module -- 3.3 SERes Detection Heads -- 4 Experimental Analysis -- 4.1 Experimental Dataset -- 4.2 Experimental Environment -- 4.3 Comprehensive Experimental Results -- 5 Conclusion -- References -- Enhanced Tiny Object Detection in Aerial Images -- 1 Introduction -- 2 Related Works -- 2.1 Multi-scale Feature Fusion -- 2.2 Context-Sensitive Feature Fusion -- 3 Methodology -- 3.1 Improved YOLOv5 Framework -- 3.2 Depth-Wise-Reshaping Module -- 3.3 Multi-branch Deep Stripe Attention Module -- 3.4 Decoupled-Head Module -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Evaluation Metrics -- 4.3 Main Results -- 4.4 Qualitative Research -- 4.5 Ablation Studies -- 5 Conclusion -- References -- Strategic Reparameterization for Enhanced Inference in Imperfect Information Games: A Neural Network Approach -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Enhanced Inferential Reasoning Framework via Reparameterization Method -- 3.2 Constructing a Normal Distribution Aligned with the Current Game Process -- 4 Experiments -- 4.1 Experimental Setup and Data Analysis Methods -- 4.2 Experimental Analysis of Military Chess -- 4.3 Experimental Analysis of Dark Chess -- 4.4 Experimental Analysis of Lord of Rings -- 5 Conclusion -- References -- TSMGAN-II: Generative Adversarial Network Based on Two-Stage Mask Transformer and Information Interaction for Speech Enhancement -- 1 Introduction -- 1.1 Background -- 1.2 Challenges -- 1.3 Contributions -- 2 Proposed Method -- 2.1 Method Description -- 2.2 Generator -- 2.3 Attention Encoder -- 2.4 Two-Stage Mask Transformer -- 2.5 Dual-Feature Decoder with Information Interaction -- 2.6 Lightweight Model.
2.7 Discriminator -- 2.8 Loss Function -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Baseline -- 3.3 Ablation Experiment -- 3.4 General Experiment -- 4 Conclusion -- References -- Prompt-Based Event Temporal Relation Extraction with Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Event Temporal Relation Extraction -- 2.2 Prompt-Based Learning -- 2.3 Contrastive Learning -- 3 Method -- 3.1 Prompt Template and Verbalizer Design -- 3.2 Prompt-Based Supervised Contrastive Loss -- 3.3 Prompt-Based Tuning -- 4 Experiment Settings -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Subdomain Adaption Network Combining Cosine Distance and Angle Margin -- 1 Introduction -- 2 Related Works -- 2.1 Domain Adaptation -- 2.2 Large Margin Cosine Loss and Additive Angular Margin Loss -- 3 Methods -- 3.1 Maximum Mean Discrepancy -- 3.2 Combined Large Margin Cosine Loss and Additive Angular Margin Loss -- 3.3 Standardized Cosine Distance Loss -- 3.4 Network Structure and Cost Function -- 4 Experiment -- 4.1 Datasets -- 4.2 Setup -- 4.3 Results -- 4.4 Ablation Study -- 4.5 Feature Visualization -- 4.6 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Self-attention-Based Dual-Branch Person Re-identification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Dual-Branch Network -- 3.2 Self-attention Model -- 4 Experiments -- 4.1 Comparison with Existing Methods -- 4.2 Ablation Experiment -- 5 Conclusion -- References -- Open-Vocabulary Object Detection by Novel-Class Feature Perception Enhancement -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pretraining by Image Masking -- 3.2 IDF Feature Extraction Network -- 4 Experiment -- 4.1 Setup -- 4.2 Comparison with State-of-the-Art Approaches -- 4.3 Ablation Study -- 5 Conclusion -- References.
PLOD-YOLO: Premium Lightweight Object Detection for Autonomous Following Robot.
Record Nr. UNINA-9910879588703321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part III / / edited by De-Shuang Huang, Zhanjun Si, Yijie Pan
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part III / / edited by De-Shuang Huang, Zhanjun Si, Yijie Pan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (XVIII, 519 p. 268 illus., 184 illus. in color.)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Computational intelligence
Computer networks
Machine learning
Application software
Computational Intelligence
Computer Communication Networks
Machine Learning
Computer and Information Systems Applications
ISBN 981-9755-88-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part III -- Neural Networks -- Trust Evaluation with Deep Learning in Online Social Networks: A State-of-the-Art Review -- 1 Introduction -- 2 Analysis and Comparison of Related Work -- 2.1 Graph-Based Neural Networks -- 2.2 Other Deep Learning Method -- 3 Challenges and Open Problems -- 4 Research Direction -- 4.1 Trust Evaluation Based on Ensemble Learning with DNN in OSNs -- 4.2 Cross-Origin Trust Evaluation Based on Deep Learning with a Hyperparameter Auto Optimizer in OSNs -- 5 Conclusion -- References -- Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review -- 1 Introduction -- 2 Analysis and Comparison of Related Work -- 2.1 Network Traffic-Based Intrusion Detection -- 2.2 Device Behavior-Based Intrusion Detection -- 3 Challenges and Open Problems -- 4 Research Direction -- 4.1 An IDS Based on Federated Learning and Transfer Learning -- 4.2 An IDS Based on Explainable DNNs -- 5 Conclusion -- References -- CNN-SENet: A Convolutional Neural Network Model for Audio Snoring Detection Based on Channel Attention Mechanism -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Convolutional Neural Networks -- 3.2 SENet -- 4 Experiment -- 4.1 Baseline -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Evaluation Indicators -- 4.5 Experimental Results -- 5 Summary -- References -- Selecting Effective Triplet Contrastive Loss for Domain Alignment -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation and Notations -- 3.2 Triplet Contrastive Loss -- 3.3 Model Structure -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Discussion -- 5 Conclusion -- References -- RCSnet--Flower Classification Network Design Based on Transfer Learning and Channel Attention Mechanism -- 1 Introduction -- 2 Related Work.
3 Methods -- 3.1 Residual Networks -- 3.2 SENet -- 3.3 Transfer Learning -- 4 Experiment -- 4.1 Baseline -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Evaluation Indicators -- 4.5 Experimental Results -- 5 Summary -- References -- MDGCL: Message Dropout Graph Contrastive Learning for Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Graph Collaborative Filtering -- 2.2 Graph Contrastive Learning -- 3 Methodology -- 3.1 Analysis of MessageDropout -- 3.2 A Simplified Architecture for GCL -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Improved CNN Model Using Innovative Adaptive-DropMessage for Gomoku Game -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Adaptive-DropMessage Method -- 3.2 Dilated Depthwise Separable Convolution -- 3.3 Residual Dense Block and SENet Module -- 3.4 Spatial Attention Module -- 3.5 Gomoku-Specific Output Layer -- 4 Experiments and Analysis -- 4.1 Experimental Setup -- 4.2 Game Experiment Results and Analysis -- 5 Conclusion -- References -- A Survey: Feature Fusion Method for Object Detection Field -- 1 Introduction -- 2 Feature Fusion Methods -- 2.1 Simple Topology Fusion Structure -- 2.2 Complex Topology Fusion Structure -- 2.3 Analysis and Summary -- 3 Datasets and Evaluation Indicators -- 3.1 Datasets -- 3.2 Evaluation Indicators -- 4 Trends and Challenges -- 5 Conclusion -- References -- Dual-Branch Collaborative Learning for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Visual Question Answering -- 2.2 Graph-Based Visual Relationship Reasoning -- 2.3 Collaborative Learning -- 3 Method -- 3.1 Overview -- 3.2 Question Representation and Image Representation -- 3.3 Relational Reasoning Branch -- 3.4 Attention Branch -- 3.5 Dual-Branch Collaborative Learning -- 3.6 Joint Predict -- 4 Experiments.
4.1 Datasets and Implementation Detail -- 4.2 Ablation Experiment -- 4.3 Comparison with SOTAs -- 4.4 Qualitative Analysis -- 5 Conclusion -- References -- SCAI: A Spectral Data Classification Framework with Adaptive Inference for Rapid and Portable Identification of Chinese Liquors -- 1 Introduction -- 2 Methodology -- 2.1 SCAI: Early-Exit with Self-distillation -- 2.2 SCAI+: SCAI with PA-ResNet -- 3 Experiment -- 3.1 Experiment Instrument -- 3.2 Datasets -- 3.3 Baselines -- 3.4 Common Performance Result -- 3.5 Application Performance Result -- 3.6 Self-distillation Training Analysis -- 4 Conclusion -- References -- EfficientPose: A Lightweight and Efficient Model with Transformer for Human Pose Estimation -- 1 Introduction -- 2 Related Work -- 2.1 Human Pose Estimation -- 2.2 Atrous Convolution and ASPP -- 3 Methods -- 3.1 EfficientPose Architecture -- 3.2 Efficient Bottleneck Block (EBB) -- 3.3 Transformer Encoder -- 3.4 Iterative Training Strategy -- 4 Experiments -- 4.1 Implementation Results -- 4.2 Ablations -- 5 Conclusion -- References -- Enhanced Chinese Named Entity Recognition with Transformer-Based Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Chinese NER Based on Lexical Enhancement -- 2.2 Chinese NER with Fusion of Glyph Features -- 2.3 Feature Fusion in Chinese NER -- 3 Method -- 3.1 Text Representation Layer -- 3.2 Sequence Encoding Layer -- 3.3 Sequence Decoding Layer -- 4 Experiments -- 4.1 Results and Analysis -- 5 Conclusion -- References -- YOLO-Fire: A Fire Detection Algorithm Based on YOLO -- 1 Introduction -- 2 YOLO-Fire -- 2.1 SimpleC3 -- 2.2 Dynamic Upsampler -- 2.3 Focal WIoU-Loss -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Ablation Studies -- 3.3 Experimental Results Analysis -- 4 Conclusion -- References -- From Vision to Sound: The Application of ViT-LSTM in Music Sequence -- 1 Introduction.
2 Related Work -- 3 Methods -- 3.1 Dataset Definition -- 3.2 Model Construction -- 4 Experiment -- 4.1 Data Processing -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- Graph Convolution Recommendation Algorithm Integrating Multi-relationship Preferences -- 1 Introduction -- 2 Graph Convolution Recommendation Algorithm Integrating Multi-relationship Preferences -- 2.1 User Feature Embedding Propagation -- 2.2 Item Feature Embedding Propagation -- 2.3 Predict -- 2.4 Model Optimization -- 3 Experimental Design and Result Analysis -- 3.1 Experimental Data Set -- 3.2 Evaluation Indicators -- 3.3 Experimental Settings -- 3.4 Experimental Results -- 3.5 Ablation Analysis -- 4 Conclusion -- References -- Temporal Sequential Wave Neural Network for Solving the Optimal Cognitive Subgraph Query Problem -- 1 Introduction -- 2 Definition of the Problem -- 3 Temporal Sequential Wave Neural Network -- 3.1 Design of Temporal Sequential Wave Neural Network -- 3.2 TSWNN Algorithm -- 3.3 Time Complexity Analysis of TSWNN Algorithm -- 4 Example of TSWNN Algorithm -- 5 Experimentation -- 5.1 Experimental Results with Different Number of Nodes -- 5.2 Experimental Results with Different Number of Sides -- 5.3 Experimental Results with Different Limiting Times -- 6 Conclusion -- References -- Joint Prior Relation Enhancement and Non-autoregressive Decoding for Document-Level Event Extraction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Entity Mention Recognition -- 3.2 Prior Relation Enhancement Encoding -- 3.3 Event Argument Combination Recognition -- 3.4 Event Type Detection -- 3.5 Event Record Extraction -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines and Metric -- 4.3 Main Results -- 4.4 Ablation Study -- 5 Conclusions -- References -- Intrusion Detection System Based on ViTCycleGAN and Rules -- 1 Introduction -- 2 Related Work.
3 Methodologies -- 3.1 Cycle-Consistent Generative Adversarial Network (CycleGAN) -- 3.2 Vision Transformer -- 3.3 Intrusion Detection System: ViTCycleGAN -- 4 Experiments -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 Performance Evaluation of ViTCyleGAN Intrusion Detection System -- 5 Conclusion -- References -- DFT-3DLaneNet: Dual-Frequency Domain Enhanced Transformer for 3D Lane Detection -- 1 Introduction -- 2 Related Work -- 2.1 2D Lane Detection -- 2.2 3D Lane Detection -- 2.3 Frequency Domain Image Processing -- 3 Method -- 3.1 Frequency Domain Feature Extraction -- 3.2 Dual-Channel High-Frequency Feature Enhancement -- 3.3 Cross-channel Low-Frequency Attention -- 3.4 Dual-Frequency Domain Deformable Attention -- 3.5 Prediction and Loss -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 4.3 Comparisons with State-of-the-Arts -- 4.4 Ablation Studies -- 5 Conclusions -- References -- Correlation Matters: A Stock Price Predication Model Based on the Graph Convolutional Network -- 1 Introduction -- 2 Related Work -- 2.1 Stock Price Prediction -- 2.2 Graph Convolutional Network -- 3 Problem Formulation -- 4 StockGCN -- 4.1 Graph Construction -- 4.2 Framework of StockGCN -- 5 Experiments -- 5.1 Datasets -- 5.2 Preprocessing -- 5.3 Baselines -- 5.4 Result Analysis -- 5.5 Ablation Study -- 6 Conclusion -- References -- Coformer: Collaborative Transformer for Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Encoder -- 3.2 Multi-scale Representation Fusion Module (MRF) -- 3.3 Decoder -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Results -- 5 Conclusion -- References -- FRMM: Feature Reprojection for Exemplar-Free Class-Incremental Learning -- 1 Introduction -- 2 Methods -- 2.1 Problem Statement and Framework -- 2.2 Feature Reprojection.
2.3 Ensemble of Two Experts.
Record Nr. UNINA-9910879579203321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part VI / / edited by De-Shuang Huang, Zhanjun Si, Jiayang Guo
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part VI / / edited by De-Shuang Huang, Zhanjun Si, Jiayang Guo
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (516 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Computational intelligence
Computer networks
Machine learning
Application software
Computational Intelligence
Computer Communication Networks
Machine Learning
Computer and Information Systems Applications
ISBN 981-9755-97-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part VI -- Image Processing -- Dual-Stream Input Gabor Convolution Network for Building Change Detection in Remote Sensing Images -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Overall Framework -- 3.2 SCM Module -- 3.3 AFM Module -- 3.4 Loss Function -- 4 Experiment Results and Analysis -- 4.1 Datasets and Experimental Platform -- 4.2 Evaluation Metric -- 4.3 The Influence of Different Parameters in Gabor Convolution -- 4.4 Comparative Experiment -- 4.5 Ablation Experiment -- 5 Conclusion -- References -- Contextual Feature Modulation Network for Efficient Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning-Based Image Super-Resolution -- 2.2 Efficient Image Super-Resolution -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Multi-scale Feature Spatial Modulation -- 3.3 Channel Attention Fusion Module -- 3.4 Feature Fusion Module -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparison to Other Methods -- 4.3 Ablation -- 5 Conclusion -- References -- Fine-Grained Image Editing Using ControlNet: Expanding Possibilities in Visual Manipulation -- 1 Introduction -- 2 Related Work -- 2.1 Image Editing -- 2.2 Image Editing -- 3 Method -- 3.1 Partial Mask -- 3.2 Spatial Features Injection -- 3.3 Classifier-Guidance-Based Editing Design -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Metrics -- 4.3 Ablation Study -- 4.4 Qualitative Evaluation -- 4.5 Quantitative Evaluation -- 5 Conclusion -- References -- MDIINet: A Few-Shot Semantic Segmentation Network by Exploiting Multi-dimensional Information Interaction -- 1 Introduction -- 2 Problem Setup -- 3 Proposed Approach -- 3.1 Inter-class Feature Fusion Module -- 3.2 High-Level Feature Extraction Module -- 3.3 Feature Fusion and Decoded Output -- 3.4 Hybrid Loss -- 4 Experiments -- 4.1 Experimental Setting.
4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusion -- References -- A Large Model Assisted Remote Sensing Image Scene Understanding Algorithm Based on Object Detection -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Network Structure of Large Model Assisted Visual Scene Understanding Algorithm -- 3.2 Prompt Engineering Based on Large Language Modelg -- 4 Experiments -- 4.1 Scene Description and Visualization Analysis of QA -- 4.2 Design of Visual Scene Understanding System -- 5 Conclusion -- References -- PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation -- 1 Introduction -- 2 Methodology -- 2.1 Overview -- 2.2 Dynamic Mix Augmentation from Heterogeneous Views -- 2.3 Uncertainty-Guided Pixel-Level Contrastive Learning -- 2.4 Regularization of Supervision via Dual Consistency -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Quantitative Comparison -- 3.3 Ablation Study -- 3.4 Visualization Analysis -- 4 Conclusions -- References -- Few-Shot Domain Adaptation via Prompt-Guided Multi-prototype Alignment Network -- 1 Introduce -- 2 Relate Work -- 3 Method -- 3.1 Prompt-Based Prototype Alignment Network -- 3.2 Domain-Specific Feature Mapping Network -- 3.3 Design of Multi-text-Guided Soft Prompts -- 4 Experiments -- 4.1 Experiment Setting -- 4.2 Implementation Details. -- 4.3 Comparison with State-Of-The-Art DA Methods -- 4.4 Ablation Study and Analyse -- 5 Conclusion -- References -- Controllable Rain Image Generation: Balance Between Diversity and Controllability -- 1 Introduction -- 2 The Proposed Method -- 2.1 Generative Model -- 2.2 Training Strategy -- 3 Experimental Results -- 3.1 Rain Generation Experiments -- 3.2 Disentanglement Experiments -- 3.3 Rain Removal Experiments -- 4 Conclusion and Future Work -- References.
Unsupervised Domain Adaptation in Medical Image Segmentation via Fourier Feature Decoupling and Multi-teacher Distillation -- 1 Introduction -- 2 Related Work -- 2.1 Unsupervised Domain Adaptation -- 2.2 Multi-teacher Knowledge Distillation Models -- 3 Method Overview -- 3.1 Feature Decoupling Based on Fourier Transform -- 3.2 Multi-teacher Knowledge Distillation Module -- 3.3 Dynamic Weighting Adaptive Strategy -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Validation of Different Models Adaptability in the Target Domain -- 4.3 Validation of the Effectiveness of Fourier Feature Decoupling Method -- 4.4 Performance Validation of Multi-teacher Knowledge Distillation Network -- 5 Conclusion -- References -- DeCoGAN: Photo Cartoonization Based on Deformation Consistency GAN -- 1 Introduction -- 2 Methodology -- 2.1 Network Architecture -- 2.2 Loss Function -- 3 Experiments -- 3.1 Dataset -- 3.2 Parameters -- 3.3 Qualitative Evaluation -- 3.4 Quantitative Evaluation -- 3.5 Ablation Study -- 4 Conclusion and Limitation -- References -- Self-supervised Siamese Networks with Squeeze-Excitation Attention for Ear Image Recognition -- 1 Introduction -- 2 Classical Convolutional Neural Network -- 3 Methods -- 3.1 Siamese Network -- 3.2 SE-SiamNet -- 4 Experimental Result -- 4.1 Dataset Description -- 4.2 Experimental Setup -- 4.3 Result Analysis -- 5 Conclusion -- References -- CtF: Mitigating Visual Confusion in Continual Learning Through a Coarse-To-Fine Screening -- 1 Introduction -- 2 Related Work -- 2.1 Continual Learning -- 2.2 Vision-Language Model -- 3 Proposed Method -- 3.1 Preliminary Analysis -- 3.2 Coarse-To-Fine Screening Framework -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Quantitative Evaluation -- 4.3 Ablation Study -- 4.4 Sensitivity Study -- 5 Conclusion -- References -- HDR Video Coding Based on Perceptual Optimization.
1 Introduction -- 2 Perceptual Optimization Model -- 2.1 Perceptual Lossless Preprocessing Model -- 2.2 Visual Perceptual Saliency -- 2.3 CTU-Level Quantization Model -- 3 Results and Analysis -- 4 Conclusion -- References -- Infrared-Visible Light Image Fusion Method Based on Weighted Salience Detection and Visual Information Preservation -- 1 Introduction -- 2 Proposed Method -- 2.1 Two-Scale Image Decomposition Based on Average Filter -- 2.2 Weighted Visual Significance Feature Extraction -- 2.3 The Final Detail Layer and Visible Image Fusion Based on Grayscale Driven -- 3 Experiments and Results -- 3.1 Experimental Setup -- 3.2 Comparative Experiment -- 4 Conclusions -- References -- CAT-DG: A Cross-Attention-Based Domain Generalization Model for Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Concepts Related to Domain Generalization -- 2.2 The FLLN-DG Model -- 2.3 Cross-Attention Mechanism -- 3 Cross-Attention-Based Segmentation Model for Domain Generalization -- 3.1 Model Architecture -- 3.2 Cross-Attention Module -- 3.3 Loss Function -- 4 Experiments and Results -- 4.1 Dataset and Parameter Settings -- 4.2 Performance Comparison of Different Models -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- Superpixel-Based Dual-Neighborhood Contrastive Graph Autoencoder for Deep Subspace Clustering of Hyperspectral Image -- 1 Introduction -- 2 Proposed Methods -- 2.1 Construction of Dual-Neighborhood Graph -- 2.2 Superpixel Dual-Neighborhood Contrastive Graph Autoencoder -- 2.3 Contrastive Learning -- 2.4 Objective Function and Optimization -- 3 Experiment -- 3.1 Experiment Setup -- 3.2 Comparison with State-of-the-Art Methods -- 3.3 Parameter Analysis -- 3.4 Ablation Study -- 4 Conclusion -- References -- SMVT: Spectrum-Driven Multi-scale Vision Transformer for Referring Image Segmentation -- 1 Introduction.
2 Related Works -- 3 Method -- 3.1 Overview -- 3.2 Cross-modal Feature Encoder -- 3.3 Spectrum-Driven Fusion Attention -- 3.4 Cross-modal Feature Refinement Enhancement Module -- 3.5 Prediction Mask and Loss Function -- 4 Experiments -- 4.1 Dataset and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with State-of-the-Art Methods -- 4.4 Ablation Study -- 5 Conclusion -- References -- GDCSF: Global Depth Convolution-Based Swin Framework for Electron Microscopy Pollen Image Classification -- 1 Introduction -- 2 Research Background -- 3 Methodology -- 3.1 Swin Transformer -- 3.2 Strip Convolution Scaling Module -- 3.3 Field Dual Bilateral Attention Module -- 3.4 Measurement -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Experimental Settings -- 4.3 Experimental Results and Inference -- 4.4 Ablation Experiments -- 5 Conclusions -- References -- Adversarial Attack Against Convolutional Neural Network via Gradient Approximation -- 1 Introduction -- 2 Related Work -- 2.1 Notions -- 2.2 Convolutional Neural Network -- 2.3 Threat Model -- 3 Methodologies -- 3.1 Model Framework -- 3.2 Gradient Optimization -- 3.3 Adversarial Sample Generation -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- Hierarchical Cascaded Multi-Axis Window Self-Attention and Layer Feature Fusion for Brain Glioma Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method Overview -- 3.1 Model Architecture -- 3.2 Hierarchical Cascaded Multi-Axis Window Self-Attention -- 3.3 Layer Feature Fusion -- 4 Experiments -- 4.1 Dataset -- 4.2 Training and Parameter Settings -- 4.3 Validation of the Effectiveness of the Hierarchical Cascaded Multi-Axis Window Self-Attention Component -- 4.4 Validation of the Effectiveness of the Layer Feature Fusion Component -- 4.5 Performance Comparison of Different Models.
5 Conclusion.
Record Nr. UNINA-9910878992303321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Chuanlei Zhang, Yijie Pan
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Chuanlei Zhang, Yijie Pan
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (508 pages)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computers
Computer networks
Data mining
Image processing - Digital techniques
Computer vision
Software engineering
Artificial Intelligence
Computing Milieux
Computer Communication Networks
Data Mining and Knowledge Discovery
Computer Imaging, Vision, Pattern Recognition and Graphics
Software Engineering
ISBN 981-9756-66-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Intelligent Data Analysis and Prediction -- Long-Short-Term Expert Attention Neural Networks for Traffic Flow Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Conventional Traffic Flow Prediction -- 2.2 Graph Neural Networks Based Traffic Flow Forecasting -- 2.3 Attention Mechanism -- 3 Method -- 3.1 Overall Network Architecture -- 3.2 Input Embedding -- 3.3 Shared Mixture of Experts -- 3.4 Long-Term Expert Networks and Short-Term Expert Network -- 3.5 Integration and Information Interaction Module -- 4 Experiments -- 4.1 Datasets and Experimental Settings -- 4.2 Performance Comparison -- 4.3 Evaluation of Long-Term Prediction -- 4.4 Effectiveness of Model Parameters and Ablation Studies -- 5 Conclusion -- References -- Capturing Dynamic Dependencies and Temporal Fluctuations for Traffic Flow Forecasting -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Learnable Traffic Embedding -- 3.2 Graph Embedding Convolutional Recurrent Network -- 3.3 Temporal Interleaved Convolution -- 3.4 Dual-Branch Gated Attention -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Baselines -- 4.4 Comparison Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- IFTNet: Interpolation Frequency- and Time-Domain Network for Long-Term Time Series Forecasting -- 1 Introduction -- 2 Preliminary -- 2.1 Picket Fence Effect and Frequency-Domain Interpolation -- 2.2 Problem Definition -- 3 Methods -- 3.1 Seasonal-Trend Decomposition -- 3.2 Feature Embedding Block -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 4.5 Parameter Sensitivity -- 4.6 Apply Frequency-Domain Interpolation to Frequency-Domain Based Model -- 4.7 Showcases -- 5 Conclusion -- References.
UNetPlusTS: Decomposition-Mixing UNet++ Architecture for Long-Term Time Series Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Linear Long-Term Time Series Forecasting -- 2.2 U-Net Series Architectures -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Architecture -- 3.3 UNetPlus Mixing Module (UPM) -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental -- 4.3 Main Results -- 4.4 Ablation Study -- 4.5 Visualization -- 4.6 Parameter Sensitivity -- 5 Conclusion -- References -- Combining Multi-granularity Text Semantics with Graph Relational Semantics for Question Retrieval in CQA -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Methodology -- 4.1 Q-Q Connection Relations Learning -- 4.2 Q-a Relevance Learning by a BERT-Based Model -- 4.3 Q-q Similarity Learning by a Tag-Enhanced Multi-granularity Matching Model -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Experimental Results -- 6 Conclusion -- References -- Frequency Enhanced Carbon Dioxide Emissions Forecasting Model with Missing Values Encoding -- 1 Introduction -- 2 Backgrounds -- 3 Missing Information Encoding Module -- 3.1 Bidirectional gated Recurrent Unit -- 3.2 Delta Coding for Missing Value Processing -- 4 Frequency Enhancement Fusion Module -- 4.1 Discrete Cosine Transform Block -- 4.2 Channel Fusion Block -- 5 Experiments and Analysis of Prediction Models -- 5.1 Carbon Dioxide Emission Data Set -- 5.2 Experimental Design -- 5.3 Comparative Analysis of Results of Different Benchmark Models -- 6 Conclusion -- References -- Short-Term PV Output Forecasting Approach Based on Deep Learning and Singular Spectrum Analysis -- 1 Introduction -- 2 Fuzzy C-Means Clustering and Singular Spectrum Analysis Based on Ant Colony Optimization -- 2.1 Ant Colony Optimization -- 2.2 Fuzzy C-Means Clustering Based on Ant Colony Optimization.
2.3 Singular Spectrum Analysis Based on Ant Colony Optimization -- 3 Deep Learning Models -- 3.1 Attention Mechanism Based on Feature -- 3.2 The CNN-BIGRU Classification Model and the Regression Forecasting Model CNN-BIGRU-ATTENTION -- 3.3 The Overall Procedure of the Proposed Forecasting Approach -- 4 Case Study and Numerical Results -- 4.1 Data Clustering Analysis -- 4.2 Decomposition Strategy -- 4.3 Forecasting Result and Discussion -- 5 Conclusions -- References -- Enhancing Stock Similarity Analysis with Phase-Embedded Multivariate Similarity Measure -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Heterogeneity-Oriented Feature Fusion -- 3.3 Phase-Embedded Time Warping -- 3.4 Prediction Schemes -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Comparison of Methods -- 4.3 Results and Analysis -- 5 Conclusions -- References -- Enhancing Federated Learning: A Novel Approach of Shapley Value Computation in Smart Contract -- 1 Introduction -- 2 System Architecture -- 2.1 Federated Learning -- 2.2 Shapley Value -- 2.3 Smart Contract -- 3 Methodology -- 3.1 Overview -- 3.2 Initial Phase -- 3.3 Model Selection and Updates -- 3.4 Model Selection and Updates -- 4 Experimental Validation of Smart Contract-Based Shapley Value Computation -- 4.1 Setup and Methodological Synthesis -- 4.2 Results Integrating Smart Contract Computations -- 4.3 Parameter Sensitivity and System Robustness -- 5 Conclusion -- References -- CPMA: Spatio-Temporal Network Prediction Model Based on Convolutional Parallel Multi-head Self-attention -- 1 Introduction -- 2 Related Work -- 3 Improved LSTM Prediction Model Based on CPMA -- 4 Experiment -- 4.1 Experimental Data -- 4.2 Model Evaluation Criteria -- 4.3 Comparative Test of Different Network Structures in the Same Sample -- 4.4 Comparative Test of Different Batch Data Input.
4.5 Comparative Experiments of Different Data Volumes -- 4.6 Comparative Experiments at Different Monitoring Points in the Same Time Period -- 4.7 Comparison of Forecasting Errors of Different Forecasting Models -- 5 Conclusion -- References -- Attention Based Multi-scale Spatial-temporal Fusion Propagation Graph Network for Traffic Flow Prediction -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Overall Architecture -- 3.2 Embedding Layer -- 3.3 Spatial-Temporal Attention -- 3.4 Multi-scale Spatial-Temporal Mix-hop Graph Convolution -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Integrating Social and Knowledge Graphs with Time Decay Mechanisms -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Initial Embedding Layer -- 3.2 Fusion Layer -- 3.3 Propagation Layers -- 3.4 Concatenation Layers -- 3.5 Prediction Layers -- 3.6 Optimization Method -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance Comparison (RQ1) -- 4.3 Ablation Study of ISKG Framework (RQ2) -- 4.4 Impact of Time Decay Function TD(t) on ISKG-TD's Efficacy (RQ3) -- 5 Conclusion and Future Work -- References -- Multi-modal Quality Prediction Algorithm Based on Anomalous Energy Tracking Attention -- 1 Introduction -- 2 Relative Works -- 2.1 Research Status of Product Quality Prediction Technology -- 2.2 Research Status of Product Image Defect Detection -- 3 Model and Design -- 3.1 Fluctuation Energy Information Extraction Module -- 3.2 Multimodal Attention Fusion Module -- 3.3 Bilinear Pooling Fusion Module -- 4 Experiments and Analysis of Results -- 4.1 Datasets and Preprocessing -- 4.2 Design of Evaluation Indicators and Parameters -- 4.3 Performance Comparison Experiments -- 4.4 Ablation Experiments -- 5 Conclusion -- References.
Hybrid Convolution Based Online Multivariate Time Series Forecasting Algorithm -- 1 Introduction -- 2 Proposed Framework -- 2.1 Problem Definition -- 2.2 Model Architecture -- 2.3 Feature Mapping Module -- 2.4 Dual Convolution Model -- 2.5 Linear Mapping Module -- 3 Experiments -- 3.1 Experimental Setting -- 3.2 Forecasting Results -- 3.3 Ablation Studies -- 3.4 Cumulative Loss Analysis -- 4 Conclusion and Future Work -- References -- Daformer: A Novel Dimension-Augmented Transformer Framework for Multivariate Time Series Forecasting -- 1 Introduction -- 2 Methodology -- 2.1 Dimension-Augmented Module -- 2.2 Feature Fusion Module -- 2.3 CTE Block -- 3 Experiments -- 3.1 Experimental Design -- 3.2 Comparative Experiments -- 3.3 Specified Periods in the Dimension-Augmented Module -- 3.4 Specified Kernel Size in the Feature Fusion Module -- 3.5 Ablation Experiments -- 3.6 Efficiency Analysis -- 3.7 Hyperparameter Analysis -- 4 Conclusion -- References -- A Transformer-Based Model for Time Series Prediction of Remote Sensing Data -- 1 First Section -- 2 Related Work -- 3 DataSet -- 4 RSformer -- 4.1 Feature Extraction -- 4.2 De-normalization -- 4.3 Series Decomposition -- 5 Experiment -- 5.1 Result -- 5.2 Ablation Study -- 6 Conclusion -- References -- A Multi-scale Indicators Carbon Emission Prediction Method Based on Decision Forests -- 1 Introduction -- 2 Methods and Data Sources -- 2.1 Data Sources and Indicators -- 2.2 Models -- 3 Analysis -- 3.1 Indicator Relations and Data Mining -- 3.2 Carbon Emission Prediction Model -- 4 Discussion -- 5 Conclusion -- References -- GD-PTCF: Prompt-Tuning Based Classification Framework for Government Data -- 1 Introduction -- 2 Related Work -- 2.1 GD Classification -- 2.2 Pre-trained Classification Models -- 3 The Proposed GD-PTCF Framework -- 3.1 Overview -- 3.2 Sample Processing -- 3.3 CPP -- 3.4 RE-Coder.
3.5 CLM.
Record Nr. UNINA-9910878984903321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part V / / edited by De-Shuang Huang, Xiankun Zhang, Jiayang Guo
Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part V / / edited by De-Shuang Huang, Xiankun Zhang, Jiayang Guo
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (XVIII, 515 p. 215 illus., 189 illus. in color.)
Disciplina 006.3
Collana Lecture Notes in Computer Science
Soggetto topico Computational intelligence
Computer networks
Machine learning
Application software
Computational Intelligence
Computer Communication Networks
Machine Learning
Computer and Information Systems Applications
ISBN 981-9755-94-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910879589703321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advanced Intelligent Computing Technology and Applications [[electronic resource] ] : 19th International Conference, ICIC 2023, Zhengzhou, China, August 10–13, 2023, Proceedings, Part IV / / edited by De-Shuang Huang, Prashan Premaratne, Baohua Jin, Boyang Qu, Kang-Hyun Jo, Abir Hussain
Advanced Intelligent Computing Technology and Applications [[electronic resource] ] : 19th International Conference, ICIC 2023, Zhengzhou, China, August 10–13, 2023, Proceedings, Part IV / / edited by De-Shuang Huang, Prashan Premaratne, Baohua Jin, Boyang Qu, Kang-Hyun Jo, Abir Hussain
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (858 pages)
Disciplina 929.605
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computational intelligence
Artificial Intelligence
Computational Intelligence
ISBN 981-9947-52-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Evolutionary Computation and Learning -- Swarm Intelligence and Optimization -- Information Security -- Theoretical Computational Intelligence and Applications -- Neural Networks -- Pattern Recognition -- Image Processing -- Biomedical Data Modeling and Mining -- Biomedical Informatics Theory and Methods -- Intelligent Computing in Computational Biology -- Intelligent Computing in Drug Design -- Knowledge Discovery and Data Mining -- Machine Learning -- Natural Language Processing and Computational Linguistics -- Deep learning methods and techniques for medical image analysis -- Intelligent Computing in Computer Vision -- Intelligent Computing in Communication Networks -- Intelligent Data Analysis & Prediction -- Expert Systems -- Reinforcement Learning -- Recent advances in deep learning methods and techniques for medical image analysis.
Record Nr. UNISA-996542664503316
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
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