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Advances in Computational Collective Intelligence : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings / / Marcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (edsitors)
| Advances in Computational Collective Intelligence : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings / / Marcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XXIX, 823 p. 348 illus., 240 illus. in color.) |
| Disciplina | 006.33 |
| Collana | Communications in computer and information science |
| Soggetto topico |
Expert systems (Computer science)
Artificial intelligence Intelligent agents (Computer software) Semantic Web Human-computer interaction |
| ISBN | 3-030-63119-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Mining and Machine Learning -- Deep Learning and Applications for Industry 4.0 -- Recommender Systems -- Computer Vision Techniques -- Decision Support and Control Systems -- Intelligent Management Information Systems -- Innovations in Intelligent Systems -- Intelligent Modeling and Simulation Approaches for Games and Real World Systems -- Experience Enhanced Intelligence to IoT -- Data Driven IoT for Smart Society -- Applications of Collective Intelligence -- Natural Language Processing -- Low Resource Languages Processing -- Computational Collective Intelligence and Natural Language Processing. |
| Record Nr. | UNISA-996465342303316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in Computational Collective Intelligence : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings / / Marcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (edsitors)
| Advances in Computational Collective Intelligence : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 - December 3, 2020, Proceedings / / Marcin Hernes, Krystian Wojtkiewicz, Edward Szczerbicki (edsitors) |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XXIX, 823 p. 348 illus., 240 illus. in color.) |
| Disciplina | 006.33 |
| Collana | Communications in computer and information science |
| Soggetto topico |
Expert systems (Computer science)
Artificial intelligence Intelligent agents (Computer software) Semantic Web Human-computer interaction |
| ISBN | 3-030-63119-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Data Mining and Machine Learning -- Deep Learning and Applications for Industry 4.0 -- Recommender Systems -- Computer Vision Techniques -- Decision Support and Control Systems -- Intelligent Management Information Systems -- Innovations in Intelligent Systems -- Intelligent Modeling and Simulation Approaches for Games and Real World Systems -- Experience Enhanced Intelligence to IoT -- Data Driven IoT for Smart Society -- Applications of Collective Intelligence -- Natural Language Processing -- Low Resource Languages Processing -- Computational Collective Intelligence and Natural Language Processing. |
| Record Nr. | UNINA-9910427697403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent Information and Database Systems : 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, Proceedings, Part II
| Intelligent Information and Database Systems : 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, Proceedings, Part II |
| Autore | Nguyen Ngoc Thanh |
| Pubbl/distr/stampa | Cham : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (766 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
TranTien Khoa
TukayevUalsher HongTzung-Pei TrawińskiBogdan SzczerbickiEdward |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Information Technology
Computer Graphics Data Mining Artificial Intelligence Computers |
| ISBN | 3-031-21967-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Machine Learning and Data Mining -- Machine Learning or Lexicon Based Sentiment Analysis Techniques on Social Media Posts -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Comparison Between Sentiment Analysis Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- A Comparative Study of Classification and Clustering Methods from Text of Books -- 1 Introduction -- 2 Related Works -- 3 Project Gutenberg -- 4 Natural Language Processing -- 4.1 Word Weighting Measures -- 5 Machine Learning Methods -- 5.1 Algorithms for Classification -- 5.2 Algorithm for Clustering -- 5.3 Measures of the Quality -- 6 Proposed Approach -- 7 Experiments -- 7.1 Experimental Design and Data Set -- 7.2 Results of Experiments -- 8 Conclusions -- References -- A Lightweight and Efficient GA-Based Model-Agnostic Feature Selection Scheme for Time Series Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Feature Selection Methods -- 2.2 GA-Based Feature Selection -- 3 GA-Based Model-Agnostic Feature Selection -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 GA-Based Feature Selector -- 3.4 Training Data Generator -- 4 Performance Evaluation -- 4.1 Evaluation Settings -- 4.2 Impact of GA-Based Feature Selector -- 4.3 Impact of Training Data Generator -- 5 Conclusion -- References -- Machine Learning Approach to Predict Metastasis in Lung Cancer Based on Radiomic Features -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Radiomics Features -- 2.3 Classification Workflow -- 3 Feature Selection Challenges -- 3.1 Multiple ROIs from the Same Patient -- 3.2 Response Variable Type -- 3.3 Small Differences Between Classes -- 4 Results -- 5 Discussion and Future Work -- References -- Covariance Controlled Bayesian Rose Trees -- 1 Introduction -- 2 Algorithm.
2.1 Hierarchical Clustering -- 2.2 Bayesian Rose Trees -- 2.3 Constraining BRT Hierarchies -- 2.4 Parameterisation -- 2.5 Depth Level as a Function of the Likelihood -- 2.6 Hierarchy Outside of Defined Clusters -- 3 Method Comparison -- 4 Conclusions -- References -- Potential of Radiomics Features for Predicting Time to Metastasis in NSCLC -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Radiomics Features -- 2.3 Data Pre-processing and Unsupervised Analysis -- 2.4 Modeling of Metastasis Free Survival -- 3 Results -- 4 Discussion and Future Work -- References -- A Survey of Network Features for Machine Learning Algorithms to Detect Network Attacks -- 1 Introduction -- 2 Background Study -- 3 Literature Survey -- 4 Shortcoming of Existing Literature -- 5 Recommendations -- References -- The Quality of Clustering Data Containing Outliers -- 1 Introduction -- 1.1 The Structure of the Paper -- 2 State of Art -- 3 Clustering Data Containing Outliers -- 3.1 Clustering Algorithms: Hierarchical AHC vs Partitional K-Means -- 3.2 Clustering Quality Indices -- 3.3 Outlier Definition -- 3.4 Outlier Detection Algorithms -- 4 Experiments -- 4.1 Data Description -- 4.2 Methodology -- 4.3 Experimental Environment -- 4.4 Results -- 4.5 Discussion -- 5 Summary -- References -- Aggregated Performance Measures for Multi-class Classification -- 1 Introduction -- 2 Method -- 2.1 Classification of a Single Data Point -- 2.2 Aggregation Over Classes and Thresholds -- 2.3 Normalisation -- 2.4 The Case of Specificity -- 2.5 The Compound Measure of Accuracy -- 3 Discussion -- References -- Prediction of Lung Cancer Survival Based on Multiomic Data -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Used in the Study -- 2.2 Feature Definition and Pre-selection -- 2.3 Variable Importance Study -- 2.4 Classification of Data -- 3 Results. 3.1 Aggregation and Dimensionality Reduction -- 3.2 Predictive Potential of Various -Omics Datasets -- 3.3 Variable Importance Study in a Multiomic Dataset -- 4 Discussion -- References -- Graph Neural Networks-Based Multilabel Classification of Citation Network -- 1 Introduction -- 2 Related Works -- 3 Dataset Description -- 4 Experiments -- 5 Multilabel Classification Approach -- 6 Conclusion and Future Works -- References -- Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases -- 1 Introduction -- 2 Related Work -- 3 The Model of Partial Periodic Pattern -- 4 Proposed Algorithm -- 4.1 3P-ECLAT Algorithm -- 5 Experimental Results -- 5.1 Evaluation of Algorithms by Varying minPS -- 5.2 Evaluation of Algorithms by Varying Per -- 5.3 Scalability Test -- 5.4 A Case Study: Finding Areas Where People Have Been Regularly Exposed to Hazardous Levels of PM2.5 Pollutant -- 6 Conclusions and Future Work -- References -- Avoiding Time Series Prediction Disbelief with Ensemble Classifiers in Multi-class Problem Spaces -- 1 Introduction -- 2 Time Series Analysis Life-Cycle -- 3 Prediction Disbelief in Acceptance Tests of Forecasting Models -- 4 Discussion -- 5 Conclusions -- References -- Speeding Up Recommender Systems Using Association Rules -- 1 Introduction -- 2 Preliminaries -- 2.1 Factorization Machines -- 2.2 Association Rules -- 2.3 Related Works -- 3 FMAR Recommender System -- 3.1 Problem Definition -- 3.2 Factorization Machine Apriori Based Model -- 3.3 Factorization Machine FP-Growth Based Model -- 4 Evaluation for FMAR -- 4.1 Performance Comparison and Analysis -- 5 Conclusions and Future Work -- References -- An Empirical Experiment on Feature Extractions Based for Speech Emotion Recognition -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Feature Extraction -- 5 Methodology -- 5.1 Input Preparation. 5.2 Classification Models -- 6 Experimental Results -- 7 Conclusion and Discussion -- References -- Parameter Distribution Ensemble Learning for Sudden Concept Drift Detection -- 1 Introduction -- 2 Methods -- 2.1 BO-ERICS Phase -- 2.2 Ensemble Phase -- 3 Experiments and Discussion -- 3.1 Datasets -- 3.2 Evaluation -- 3.3 Results -- 3.4 Discussion -- 4 Conclusions -- References -- MLP-Mixer Approach for Corn Leaf Diseases Classification -- 1 Introduction -- 2 Related Work -- 2.1 Literature Review -- 2.2 MLP-Mixer -- 2.3 Deep Learning -- 3 Methods -- 3.1 Data Requirements, Collection and Preparation -- 3.2 Configure the Hyperparameters -- 3.3 Build a Classification Model -- 3.4 Define an Experiment and Data Augmentation -- 3.5 The MLP-Mixer Model Structure -- 3.6 Build, Train, and Evaluate the MLP-Mixer Model -- 4 Experiment and Result -- 4.1 Image Segmentation -- 4.2 Experiment Results (Train and Evaluate Model) -- 4.3 Discussion -- 5 Conclusion -- References -- A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations -- 1 Introduction -- 2 Related Work -- 3 A Novel Training Method with Semi-Pseudo-Labeling and 3D Augmentations -- 3.1 Semi-Pseudo-Labeling -- 3.2 3D Augmentations -- 3.3 An Example of Training with Semi-Pseudo-Labeling and 3D Augmentations -- 4 Experiments -- 4.1 Argoverse -- 4.2 In-House Highway Dataset -- 5 Conclusion -- References -- Machine Learning Methods for BIM Data -- 1 Introduction -- 2 BIM Data - IFC Files -- 3 Machine Learning Techniques for BIM -- 3.1 Learning Semantic Information - Space Classification -- 3.2 Semantic Enrichment of BIM Models from Point Clouds -- 3.3 Building Condition Diagnosis -- 3.4 BIM Enhancement in the Facility Management Context -- 3.5 Knowledge Extraction from BIM -- 4 Conclusions -- References. Self-Optimizing Neural Network in Classification of Real Valued Experimental Data -- 1 Introduction -- 2 Self Optimizing Neural Network -- 2.1 SONN Formalism -- 2.2 Fundamental Coefficient of Discrimination -- 2.3 Structure of the Network and the Weight Factor -- 2.4 Network Response -- 3 Experiment and Results -- 3.1 Dataset -- 3.2 Data Preparation -- 3.3 Classification -- 4 Conclusion -- References -- Analyzing the Effectiveness of the Gaussian Mixture Model Clustering Algorithm in Software Enhancement Effort Estimation -- 1 Introduction -- 2 Backgrounds -- 2.1 The FPA Overview -- 2.2 The Gaussian Mixture Model Clustering Algorithm -- 2.3 The k-means Clustering Algorithm -- 3 Research Methodology -- 3.1 Dataset Pre-processing -- 3.2 Determine the Number of Clusters -- 3.3 Evaluation Criteria -- 4 Results and Discussions -- 5 Conclusion -- References -- Graph Classification via Graph Structure Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Method: GC-GSL -- 3.1 Extracting Topological Attribute Vector -- 3.2 Rooted Subgraph Mining -- 3.3 Neural Network Graph Embedding -- 3.4 Computational Complexity -- 4 Experiments -- 4.1 Results -- 4.2 Discussions -- 5 Conclusion -- References -- Relearning Ensemble Selection Based on New Generated Features -- 1 Introduction -- 2 Related Works -- 3 The Proposed Framework -- 3.1 Generation of Diverse Base Classifiers -- 3.2 Relearning Base Classifiers -- 3.3 Feature Generation Based on Learned and Relearned Base Classifiers -- 3.4 Learning Second-Level Base Classifier Based on New Vector of the Features -- 3.5 Selection Base Classifiers Based on Second-Level Classification Result -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Discussion -- 6 Conclusions -- References -- Random Forest in Whitelist-Based ATM Security -- 1 Introduction -- 2 Related Work -- 3 Test Procedure. 4 Data Pre-processing. |
| Record Nr. | UNISA-996503470803316 |
Nguyen Ngoc Thanh
|
||
| Cham : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Intelligent Information and Database Systems : 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, Proceedings, Part II
| Intelligent Information and Database Systems : 14th Asian Conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, Proceedings, Part II |
| Autore | Nguyen Ngoc Thanh (Computer scientist) |
| Pubbl/distr/stampa | Cham : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (766 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
TranTien Khoa
TukayevUalsher HongTzung-Pei TrawińskiBogdan SzczerbickiEdward |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato |
Information Technology
Computer Graphics Data Mining Artificial Intelligence Computers |
| ISBN | 3-031-21967-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Machine Learning and Data Mining -- Machine Learning or Lexicon Based Sentiment Analysis Techniques on Social Media Posts -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Comparison Between Sentiment Analysis Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- A Comparative Study of Classification and Clustering Methods from Text of Books -- 1 Introduction -- 2 Related Works -- 3 Project Gutenberg -- 4 Natural Language Processing -- 4.1 Word Weighting Measures -- 5 Machine Learning Methods -- 5.1 Algorithms for Classification -- 5.2 Algorithm for Clustering -- 5.3 Measures of the Quality -- 6 Proposed Approach -- 7 Experiments -- 7.1 Experimental Design and Data Set -- 7.2 Results of Experiments -- 8 Conclusions -- References -- A Lightweight and Efficient GA-Based Model-Agnostic Feature Selection Scheme for Time Series Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Feature Selection Methods -- 2.2 GA-Based Feature Selection -- 3 GA-Based Model-Agnostic Feature Selection -- 3.1 Problem Formulation -- 3.2 Overview -- 3.3 GA-Based Feature Selector -- 3.4 Training Data Generator -- 4 Performance Evaluation -- 4.1 Evaluation Settings -- 4.2 Impact of GA-Based Feature Selector -- 4.3 Impact of Training Data Generator -- 5 Conclusion -- References -- Machine Learning Approach to Predict Metastasis in Lung Cancer Based on Radiomic Features -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Radiomics Features -- 2.3 Classification Workflow -- 3 Feature Selection Challenges -- 3.1 Multiple ROIs from the Same Patient -- 3.2 Response Variable Type -- 3.3 Small Differences Between Classes -- 4 Results -- 5 Discussion and Future Work -- References -- Covariance Controlled Bayesian Rose Trees -- 1 Introduction -- 2 Algorithm.
2.1 Hierarchical Clustering -- 2.2 Bayesian Rose Trees -- 2.3 Constraining BRT Hierarchies -- 2.4 Parameterisation -- 2.5 Depth Level as a Function of the Likelihood -- 2.6 Hierarchy Outside of Defined Clusters -- 3 Method Comparison -- 4 Conclusions -- References -- Potential of Radiomics Features for Predicting Time to Metastasis in NSCLC -- 1 Background -- 2 Materials and Methods -- 2.1 Data -- 2.2 Radiomics Features -- 2.3 Data Pre-processing and Unsupervised Analysis -- 2.4 Modeling of Metastasis Free Survival -- 3 Results -- 4 Discussion and Future Work -- References -- A Survey of Network Features for Machine Learning Algorithms to Detect Network Attacks -- 1 Introduction -- 2 Background Study -- 3 Literature Survey -- 4 Shortcoming of Existing Literature -- 5 Recommendations -- References -- The Quality of Clustering Data Containing Outliers -- 1 Introduction -- 1.1 The Structure of the Paper -- 2 State of Art -- 3 Clustering Data Containing Outliers -- 3.1 Clustering Algorithms: Hierarchical AHC vs Partitional K-Means -- 3.2 Clustering Quality Indices -- 3.3 Outlier Definition -- 3.4 Outlier Detection Algorithms -- 4 Experiments -- 4.1 Data Description -- 4.2 Methodology -- 4.3 Experimental Environment -- 4.4 Results -- 4.5 Discussion -- 5 Summary -- References -- Aggregated Performance Measures for Multi-class Classification -- 1 Introduction -- 2 Method -- 2.1 Classification of a Single Data Point -- 2.2 Aggregation Over Classes and Thresholds -- 2.3 Normalisation -- 2.4 The Case of Specificity -- 2.5 The Compound Measure of Accuracy -- 3 Discussion -- References -- Prediction of Lung Cancer Survival Based on Multiomic Data -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Used in the Study -- 2.2 Feature Definition and Pre-selection -- 2.3 Variable Importance Study -- 2.4 Classification of Data -- 3 Results. 3.1 Aggregation and Dimensionality Reduction -- 3.2 Predictive Potential of Various -Omics Datasets -- 3.3 Variable Importance Study in a Multiomic Dataset -- 4 Discussion -- References -- Graph Neural Networks-Based Multilabel Classification of Citation Network -- 1 Introduction -- 2 Related Works -- 3 Dataset Description -- 4 Experiments -- 5 Multilabel Classification Approach -- 6 Conclusion and Future Works -- References -- Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases -- 1 Introduction -- 2 Related Work -- 3 The Model of Partial Periodic Pattern -- 4 Proposed Algorithm -- 4.1 3P-ECLAT Algorithm -- 5 Experimental Results -- 5.1 Evaluation of Algorithms by Varying minPS -- 5.2 Evaluation of Algorithms by Varying Per -- 5.3 Scalability Test -- 5.4 A Case Study: Finding Areas Where People Have Been Regularly Exposed to Hazardous Levels of PM2.5 Pollutant -- 6 Conclusions and Future Work -- References -- Avoiding Time Series Prediction Disbelief with Ensemble Classifiers in Multi-class Problem Spaces -- 1 Introduction -- 2 Time Series Analysis Life-Cycle -- 3 Prediction Disbelief in Acceptance Tests of Forecasting Models -- 4 Discussion -- 5 Conclusions -- References -- Speeding Up Recommender Systems Using Association Rules -- 1 Introduction -- 2 Preliminaries -- 2.1 Factorization Machines -- 2.2 Association Rules -- 2.3 Related Works -- 3 FMAR Recommender System -- 3.1 Problem Definition -- 3.2 Factorization Machine Apriori Based Model -- 3.3 Factorization Machine FP-Growth Based Model -- 4 Evaluation for FMAR -- 4.1 Performance Comparison and Analysis -- 5 Conclusions and Future Work -- References -- An Empirical Experiment on Feature Extractions Based for Speech Emotion Recognition -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Feature Extraction -- 5 Methodology -- 5.1 Input Preparation. 5.2 Classification Models -- 6 Experimental Results -- 7 Conclusion and Discussion -- References -- Parameter Distribution Ensemble Learning for Sudden Concept Drift Detection -- 1 Introduction -- 2 Methods -- 2.1 BO-ERICS Phase -- 2.2 Ensemble Phase -- 3 Experiments and Discussion -- 3.1 Datasets -- 3.2 Evaluation -- 3.3 Results -- 3.4 Discussion -- 4 Conclusions -- References -- MLP-Mixer Approach for Corn Leaf Diseases Classification -- 1 Introduction -- 2 Related Work -- 2.1 Literature Review -- 2.2 MLP-Mixer -- 2.3 Deep Learning -- 3 Methods -- 3.1 Data Requirements, Collection and Preparation -- 3.2 Configure the Hyperparameters -- 3.3 Build a Classification Model -- 3.4 Define an Experiment and Data Augmentation -- 3.5 The MLP-Mixer Model Structure -- 3.6 Build, Train, and Evaluate the MLP-Mixer Model -- 4 Experiment and Result -- 4.1 Image Segmentation -- 4.2 Experiment Results (Train and Evaluate Model) -- 4.3 Discussion -- 5 Conclusion -- References -- A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations -- 1 Introduction -- 2 Related Work -- 3 A Novel Training Method with Semi-Pseudo-Labeling and 3D Augmentations -- 3.1 Semi-Pseudo-Labeling -- 3.2 3D Augmentations -- 3.3 An Example of Training with Semi-Pseudo-Labeling and 3D Augmentations -- 4 Experiments -- 4.1 Argoverse -- 4.2 In-House Highway Dataset -- 5 Conclusion -- References -- Machine Learning Methods for BIM Data -- 1 Introduction -- 2 BIM Data - IFC Files -- 3 Machine Learning Techniques for BIM -- 3.1 Learning Semantic Information - Space Classification -- 3.2 Semantic Enrichment of BIM Models from Point Clouds -- 3.3 Building Condition Diagnosis -- 3.4 BIM Enhancement in the Facility Management Context -- 3.5 Knowledge Extraction from BIM -- 4 Conclusions -- References. Self-Optimizing Neural Network in Classification of Real Valued Experimental Data -- 1 Introduction -- 2 Self Optimizing Neural Network -- 2.1 SONN Formalism -- 2.2 Fundamental Coefficient of Discrimination -- 2.3 Structure of the Network and the Weight Factor -- 2.4 Network Response -- 3 Experiment and Results -- 3.1 Dataset -- 3.2 Data Preparation -- 3.3 Classification -- 4 Conclusion -- References -- Analyzing the Effectiveness of the Gaussian Mixture Model Clustering Algorithm in Software Enhancement Effort Estimation -- 1 Introduction -- 2 Backgrounds -- 2.1 The FPA Overview -- 2.2 The Gaussian Mixture Model Clustering Algorithm -- 2.3 The k-means Clustering Algorithm -- 3 Research Methodology -- 3.1 Dataset Pre-processing -- 3.2 Determine the Number of Clusters -- 3.3 Evaluation Criteria -- 4 Results and Discussions -- 5 Conclusion -- References -- Graph Classification via Graph Structure Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Method: GC-GSL -- 3.1 Extracting Topological Attribute Vector -- 3.2 Rooted Subgraph Mining -- 3.3 Neural Network Graph Embedding -- 3.4 Computational Complexity -- 4 Experiments -- 4.1 Results -- 4.2 Discussions -- 5 Conclusion -- References -- Relearning Ensemble Selection Based on New Generated Features -- 1 Introduction -- 2 Related Works -- 3 The Proposed Framework -- 3.1 Generation of Diverse Base Classifiers -- 3.2 Relearning Base Classifiers -- 3.3 Feature Generation Based on Learned and Relearned Base Classifiers -- 3.4 Learning Second-Level Base Classifier Based on New Vector of the Features -- 3.5 Selection Base Classifiers Based on Second-Level Classification Result -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Discussion -- 6 Conclusions -- References -- Random Forest in Whitelist-Based ATM Security -- 1 Introduction -- 2 Related Work -- 3 Test Procedure. 4 Data Pre-processing. |
| Record Nr. | UNINA-9910634044103321 |
|
Nguyen Ngoc Thanh (Computer scientist)
|
||
| Cham : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Intelligent Information and Database Systems: Recent Developments / / edited by Maciej Huk, Marcin Maleszka, Edward Szczerbicki
| Intelligent Information and Database Systems: Recent Developments / / edited by Maciej Huk, Marcin Maleszka, Edward Szczerbicki |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XVIII, 438 p. 164 illus., 119 illus. in color.) |
| Disciplina |
001.535
006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Engineering—Data processing Database management Computational Intelligence Data Engineering Database Management |
| ISBN | 3-030-14132-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- Part I: Sensor Clouds and Internet of Things -- Chapter 1. Proposing Smart Disaster Management in Urban Area -- Chapter 2. Ubiquitous rehabilitation combining inertial measurement unit with smartphone and supported by visual and voice feedback -- Chapter 3. Decision Making Based on IoT Data Collection for Precision Agriculture -- Chapter 4. Meeting Smart City Latency Demands with SDN, etc. |
| Record Nr. | UNINA-9910484279403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge Management and Engineering with Decisional DNA / / edited by Edward Szczerbicki, Cesar Sanin
| Knowledge Management and Engineering with Decisional DNA / / edited by Edward Szczerbicki, Cesar Sanin |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (262 pages) |
| Disciplina | 658.4038 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-030-39601-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Set of Experience and Decisional DNA: Experience-based Knowledge Structures -- Visual Content Representation for Cognitive Systems: Towards Augmented Intelligence -- Smart Decisional DNA technology to enhance Industry 4.0 environment in conjunction with conventional manufacturing -- Smart Embedded Systems with Decisional DNA Knowledge Representation. |
| Record Nr. | UNINA-9910483928703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Recent challenges in intelligent information and database systems : 14th Asian conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, proceedings / / edited by Edward Szczerbicki [and four others]
| Recent challenges in intelligent information and database systems : 14th Asian conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, proceedings / / edited by Edward Szczerbicki [and four others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (793 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Database management |
| ISBN | 981-19-8234-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996500672603316 |
| Gateway East, Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Recent challenges in intelligent information and database systems : 14th Asian conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, proceedings / / edited by Edward Szczerbicki [and four others]
| Recent challenges in intelligent information and database systems : 14th Asian conference, ACIIDS 2022, Ho Chi Minh City, Vietnam, November 28-30, 2022, proceedings / / edited by Edward Szczerbicki [and four others] |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (793 pages) |
| Disciplina | 006.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Database management |
| ISBN | 981-19-8234-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910634101003321 |
| Gateway East, Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||