Intelligent information and database systems . Part I : 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, proceeding / / Ngoc Thanh Nguyen [and five others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (743 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Database management
• Persona (resp. second.): NguyenNgoc Thanh (Computer scientist)
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Advanced Data Mining Techniques and Applications -- Textual One-Pass Stream Clustering with Automated Distance Threshold Adaption -- 1 Introduction -- 1.1 Recent Work on Textual Stream Clustering -- 2 Distance Based Clustering with Automatic Threshold Determination (textClust) -- 2.1 Automatic Tresholding During the Online Phase -- 2.2 Algorithm Specification -- 3 Experiments -- 3.1 Benchmarking Datasets -- 3.2 Experimental Setup -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Future Work -- References -- Using GPUs to Speed Up Genetic-Fuzzy Data Mining with Evaluation on All Large Itemsets -- 1 Introduction -- 2 Related Work -- 3 Components of the Proposed Algorithm -- 3.1 Chromosome Representation -- 3.2 Population Initialization -- 3.3 Fitness Function and Selection -- 3.4 Genetic Operators and Termination -- 4 The Proposed GFM-GPU-LAll Optimization Algorithm -- 5 Experimental Evaluations -- 6 Conclusions and Future Work -- References -- Efficient Classification with Counterfactual Reasoning and Active Learning -- 1 Introduction -- 2 Related Works -- 3 Framework -- 3.1 Problem Definition -- 3.2 Proposed Method CCRAL -- 4 Experiments and Discussions -- 4.1 Datasets -- 4.2 Baselines and Evaluation -- 4.3 Results -- 5 Conclusion -- References -- Visual Localization Based on Deep Learning - Take Southern Branch of the National Palace Museum for Example -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network -- 2.2 Visual Localization Based on Deep Learning -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Loss Function -- 4 Experiments -- 4.1 Pretrained Model -- 4.2 Normalization -- 4.3 Loss Function -- 5 Conclusion and Future Work -- References. ; SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation System -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Contrastive Learning -- 3.2 Modeling -- 3.3 Evaluation Metrics -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Datasets -- 4.3 Training Details -- 4.4 Results -- 5 Conclusion and Further Works -- References -- Frequent Closed Subgraph Mining: A Multi-thread Approach -- 1 Introduction -- 2 Related Work -- 3 Definitions -- 4 Proposed Method -- 5 Experimental Results -- 6 Conclusion and Future Work -- References -- Decision Support and Control Systems -- Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis (SA) -- 2.2 Complement Naïve Bayes Classifier -- 2.3 Analysis Metrics -- 3 Methodology -- 3.1 Research Workflow -- 3.2 Internet Movie Database (IMDB) -- 4 Experiment and Result -- 4.1 Experiment Results -- 5 Conclusions -- References -- Portfolio Investments in the Forex Market -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 The Investing Process -- 4 Numerical Experiments -- 5 Conclusions -- References -- Detecting True and Declarative Facial Emotions by Changes in Nonlinear Dynamics of Eye Movements -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussions -- 5 Conclusions -- References -- Impact of Radiomap Interpolation on Accuracy of Fingerprinting Algorithms -- 1 Introduction -- 2 Related Work -- 2.1 Fingerprinting Localization -- 2.2 Dynamic Radiomap -- 2.3 Interpolation Algorithms -- 3 Experimental Scenario and Achieved Results -- 4 Conclusions -- References -- Rough Set Rules (RSR) Predominantly Based on Cognitive Tests Can Predict Alzheimer's Related Dementia -- 1 Introduction -- 2 Methods -- 2.1 Theoretical Basis -- 3 Results -- 3.1 Statistical Results. ; 3.2 RSR for Reference of Model1 Group -- 3.3 RSR for Reference of Model2 Group -- 4 Discussion -- References -- Experiments with Solving Mountain Car Problem Using State Discretization and Q-Learning -- 1 Introduction -- 2 Related Works -- 3 Modeling the Mountain Car Problem -- 3.1 Physics of the Mountain Car Problem -- 3.2 Model Exploration Using Random Walk and Numerical Simulation -- 4 Optimal Control Using State Discretization and Q-Learning -- 4.1 Q-Learning and SARSA Algorithms -- 4.2 State Discretization -- 4.3 Experimental Results -- 5 Conclusions and Future Work -- References -- A Stable Method for Detecting Driver Maneuvers Using a Rule Classifier -- 1 Introduction -- 2 Data Logging -- 2.1 Data Stream Forming -- 2.2 Data Collection -- 3 Evaluation of the Model -- 4 Conclusions and Further Work -- References -- Deep Learning Models -- Using Deep Transformer Based Models to Predict Ozone Levels -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Baseline Models -- 3.2 Performance Evaluation Metrics -- 4 Problem Description and Our Model -- 4.1 Problem Description -- 4.2 Deep Transformer Based Models -- 4.3 MLP and LSTM Networks -- 5 Experiments -- 5.1 Comparison Between Models -- 5.2 Hyperparameters Optimisation -- 6 Conclusions and Future Work -- References -- An Ensemble Based Deep Learning Framework to Detect and Deceive XSS and SQL Injection Attacks -- 1 Introduction -- 1.1 Background Study -- 2 Proposed Detection and Deception Technique -- 2.1 Data Preparation and Feature Selection -- 2.2 Using the Ensemble Based Deep Learning Classifiers -- 2.3 State Maintenance Module -- 2.4 Deception Module to Lure/Engage Attackers -- 3 Discussion, Performance Analysis and Testing -- 3.1 Comparative Analysis -- 4 Conclusion and Future Work -- References -- An Image Pixel Interval Power (IPIP) Method Using Deep Learning Classification Models. ; 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Baseline Method -- 4.3 Training Setup -- 4.4 Evaluation Metrics -- 4.5 Experimental Results and Discussions -- 5 Conclusion -- References -- Meta-learning and Personalization Layer in Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Numerical Experiments -- 5 Results and Discussion -- 6 Conclusion -- A Experimental Details -- A.1 Model Architecture -- A.2 Hyper-parameters Searching -- References -- ETop3PPE: EPOCh's Top-Three Prediction Probability Ensemble Method for Deep Learning Classification Models -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Training Setup -- 4.3 Evaluation Metrics -- 4.4 Experiment Results and Discussions -- 5 Conclusions -- References -- Embedding Model with Attention over Convolution Kernels and Dynamic Mapping Matrix for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Dynamic Convolution -- 3.2 TransD Model -- 4 The Proposed Model -- 5 Experiments and Result Analysis -- 5.1 Benchmark Datasets -- 5.2 Experimental Setup -- 5.3 Results -- 6 Conclusion and Future Research Directions -- References -- Employing Generative Adversarial Network in COVID-19 Diagnosis -- 1 Introduction -- 2 Proposed Framework -- 2.1 Data Augmentation -- 2.2 Transfer Learning -- 3 Experimental Evaluation -- 3.1 Using GAN to Generate Synthetic Images -- 3.2 Transfer Learning -- 4 Conclusion -- References -- SDG-Meter: A Deep Learning Based Tool for Automatic Text Classification of the Sustainable Development Goals -- 1 Introduction -- 2 State-of-the-Art -- 3 Multi-labeled Text Classification with BERT -- 3.1 BERT: Bidirectional Encoder Representations from Transformers -- 3.2 SDG-Meter Tool -- 4 Experimentation. ; 4.1 Dataset -- 4.2 Test and Results -- 5 Conclusion -- References -- The Combination of Background Subtraction and Convolutional Neural Network for Product Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Background Subtraction and Skin Removal -- 3.2 Product Classification -- 3.3 Product Tracking and Counting -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Training Classifier -- 4.3 Result and Discussion -- 5 Conclusions -- References -- Strategy and Feasibility Study for the Construction of High Resolution Images Adversarial Against Convolutional Neural Networks -- 1 Introduction -- 1.1 Attacks in the R Domain -- 1.2 Three Challenges Faced by Attacks in the H Domain -- 1.3 Our Contribution: A Strategy and a Feasibility Study -- 2 CNNs and the Target Scenario -- 2.1 The Target Scenario -- 2.2 The Target Scenario Lifted to HR Images -- 3 Attack Strategy for the Target Scenario on HR Images -- 3.1 Construction of Adversarial Images in H -- 3.2 Indicators: The Loss Function L and L2-distances -- 4 Feasibility Study -- 4.1 The Evolutionary Algorithm EAtarget,C -- 4.2 Running the Strategy to Get Adversarial Images with the EA -- 4.3 Visual Quality -- 5 Conclusion -- References -- Using Deep Learning to Detect Anomalies in Traffic Flow -- 1 Introduction -- 2 Problem Description -- 2.1 Data -- 2.2 Scenarios -- 3 Auto-encoder Models -- 3.1 CNN Auto-encoder Model -- 3.2 BiLSTM Auto-encoder Model -- 4 Experiments -- 4.1 Basic Scenario -- 4.2 Guided Scenario -- 5 Conclusions and Future Work -- References -- A Deep Convolution Generative Adversarial Network for the Production of Images of Human Faces -- 1 Introduction -- 2 A Recall of the Genarative Adversial Networks (GAN) -- 3 Related Works Concerning the Variants of GANs -- 3.1 Architecture-Variant -- 3.2 Loss-Variant. ; 4 Deep Convolutional GAN: A Method Adopted for Human Faces Images Producing.
• Titolo autorizzato: Intelligent Information and Database Systems
• ISBN: 3-031-21743-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996503471103316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture notes in computer science.