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Computational Collective Intelligence : 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings
| Computational Collective Intelligence : 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings |
| Autore | Nguyen Ngoc Thanh (Computer scientist) |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2022 |
| Descrizione fisica | 1 online resource (863 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
ManolopoulosYannis
ChbeirRichard KozierkiewiczAdrianna TrawińskiBogdan |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato | Science |
| ISBN | 3-031-16014-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Collective Intelligence and Collective Decision-Making -- Inferring Event Causality in Films via Common Knowledge Corpora -- 1 Introduction -- 1.1 Background -- 1.2 Research Objectives and Contributions -- 2 Background and Related Work -- 2.1 Event Causality in Films -- 2.2 Computational Methods -- 3 Event Causality Inference System -- 4 Evaluation Experiments -- 5 Discussion -- References -- Cooperation Game on Communication Multigraph with Fuzzy Parameters -- 1 Introduction -- 2 Preliminaries -- 3 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities -- 4 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities and Fuzzy Goal -- 5 Conclusions and Future Works -- References -- Impact of Similarity Measure on the Quality of Communities Detected in Social Network by Hierarchical Clustering -- 1 Introduction -- 2 Organizational Social Networks -- 3 Community Detection Problem -- 4 Hierarchical Clustering Approach to the Community Detection -- 4.1 Hierarchical Clustering -- 4.2 Similarity Measures in Social Networks -- 4.3 Hierarchical Clustering Approach to the Community Detection in Organizational Social Network -- 5 Computational Experiment -- 6 Conclusions -- References -- An Approach to Modeling a Real-Time Updated Environment Based on Messages from Agents -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Environment Model Assumptions -- 3.2 Environment Model -- 4 Case Study: AriaDNA Life System -- 4.1 Space and Dimensions -- 4.2 Entity Types -- 4.3 Messages -- 4.4 Interactions -- 5 Summary -- References -- Updating the Result Ontology Integration at the Concept Level in the Event of the Evolution of Their Components -- 1 Introduction -- 2 Related Works.
3 Basic Notions -- 4 Methods of Updating Integrated Ontologies -- 4.1 Updating Results of Ontology Integration After Concept Removal -- 4.2 Updating Results of Ontology Integration After Adding New Concepts -- 4.3 Updating Results of Ontology Integration After Concepts Modification -- 5 Experimental Evaluation -- 6 Summary -- References -- Integrating Machine Learning into Learner Profiling for Adaptive and Gamified Learning System -- 1 Introduction -- 2 Proposed Method -- 2.1 Functional Architecture -- 3 Experimentation and Results -- 3.1 Used Method -- 3.2 Results -- 4 Conclusion -- References -- Deep Learning Techniques -- A New Deep Learning Fusion Approach for Emotion Recognition Based on Face and Text -- 1 Introduction -- 2 Facial-Textual Emotion Recognition (FTxER) Architecture -- 3 Results and Discussion -- 4 Conclusion and Future Work -- References -- Cycle Route Signs Detection Using Deep Learning -- 1 Introduction -- 2 Problem Definition -- 2.1 Car License Plate Recognition and Reading -- 2.2 Real-time Traffic Sign Detection Using CNN -- 3 Solution -- 3.1 YOLO -- 3.2 OCR -- 4 Implementation -- 4.1 Technologies Used -- 4.2 Data Training -- 4.3 Applications -- 5 Testing of Developed Application -- 6 Conclusion -- References -- Data Augmentation for Morphological Analysis of Histopathological Images Using Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Morphing Concept -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework -- 4 Experiments and Results -- 4.1 Experimental Corpus -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions and Further Work -- References. Deep Convolutional Neural Network for Arabic Speech Recognition -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 3.1 Convolutional Neural Network (CNN) -- 3.2 LSTM -- 4 Experimental Results and Discussion -- 4.1 Datasets and Input Features -- 4.2 Results and Discussion -- 5 Conclusion and Future Works -- References -- RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval -- 1 Introduction -- 2 Related Works -- 3 RingNet Neural Network -- 3.1 Descriptors Calculation -- 3.2 RingNet Layer -- 3.3 Fusion Layer -- 4 Experimental Results -- 4.1 Dataset and Metrics -- 4.2 3D Protein Shape Classification -- 4.3 3D Protein Shape Retrieval -- 5 Conclusion -- References -- Patch Selection for Melanoma Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description and Preprocessing -- 2.2 Entropy -- 2.3 Mean Exhaustive Minimum Distance (MEMD) Criterion -- 2.4 Network Architecture -- 3 Experimental Results -- 4 Conclusion -- References -- Natural Language Processing -- Multi-model Analysis of Language-Agnostic Sentiment Classification on MultiEmo Data -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Pipeline -- 3.2 MultiEmo Dataset -- 3.3 Scenarios -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tunisian Users on Social Networks: Overcoming the Challenge of Multilingual Comments in the Tunisian Dialect -- Abstract -- 1 Introduction -- 2 Specificities of Tunisian Dialect Sentiment Analysis -- 2.1 The Use of Multilingual Vocabulary -- 2.2 The Phenomenon of Linguistic Code-switching -- 3 Background and Related Work -- 3.1 Arabic Dialects Sentiment Analysis -- 3.2 Tunisian Dialect Sentiment Analysis -- 3.3 Discussion -- 4 Proposed Methodology -- 4.1 Data Collection -- 4.2 Data Preprocessing -- 4.3 Model Training. 5 Experiments and Results -- 5.1 Baseline -- 5.2 Dataset -- 5.3 Experimental Results -- 5.4 Discussion -- 6 Conclusion -- Acknowledgment -- References -- Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Non-Contextual Dataset -- 3.2 Contextual Dataset -- 4 Methods for Multiword Expression Detection -- 5 Experiments -- 6 Results -- 7 Discussion -- 8 Conclusions and Future Work -- References -- Context-free Transformer-based Generative Lemmatiser for Polish -- 1 Introduction -- 2 Related Works -- 2.1 Contextual Lemmatisation -- 2.2 Context-free Lemmatisation -- 2.3 Lemmatisation for the Polish Language -- 3 Problem Description -- 4 Architecture -- 4.1 Transformer-based Lemmatiser -- 5 Experiments -- 6 Conclusions -- References -- French Object Clitics in the Interlanguage: A Linguistic Description and a Formal Analysis in the ACCG Framework -- 1 Introduction -- 2 Interlanguage -- 2.1 The Concept -- 2.2 French Object Clitics in the Interlanguage -- 3 Applicative Combinatory Categorical Grammar -- 4 Object Clitics by Means of ACCG -- 5 Conclusion -- References -- Contradiction Detection Approach Based on Semantic Relations and Evidence of Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Motivation and Proposed Model -- 3.1 Motivation -- 3.2 Description of the Approach -- 4 Semantic Construction of a Sentence -- 4.1 Pretreatment -- 4.2 Concept Extraction -- 4.3 Extraction of Uncertainty Expressions -- 4.4 Calculation of Degree of Uncertainty -- 4.5 Extraction of Binary Relations -- 5 Contradiction Assessment -- 5.1 Detection of Opposing Information -- 5.2 Detection of Contradiction -- 6 Experimental Evaluation -- 6.1 Test Environment -- 6.2 Experiments and Results -- 7 Conclusion -- References -- C-DESERT Score for Arabic Text Summary Evaluation -- Abstract. 1 Introduction -- 2 Related works -- 3 Proposed method -- 3.1 Document Embedding Model -- 3.2 Building the Doc2Vec Model -- 3.3 Features -- 3.4 Combination Scheme -- 4 Experiments -- 4.1 Data Sets -- 4.2 Result -- 5 Conclusion -- References -- Data Mining and Machine Learning -- Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models -- 1 Introduction -- 2 Related Works -- 3 Experiments -- 3.1 Datasets -- 3.2 Features -- 3.3 Methods -- 3.4 Tool Implementation -- 3.5 Experiment Scenarios -- 4 Results and Analysis -- 4.1 Experiment One -- 4.2 Experiment Two -- 4.3 Experiment Three -- 5 Conclusions -- References -- Simulation System for Producing Real World Dataset to Predict the Covid-19 Contamination Process⋆ -- 1 Introduction -- 2 Contact Tracing -- 2.1 GPS Accuracy -- 2.2 Bluetooth LE Distance Measurement -- 2.3 WiFi -- 2.4 Zigbee -- 2.5 Comparing Technologies -- 3 Dataset Collection -- 3.1 Simulating Real World Scene -- 4 Processing Data -- 4.1 Graph Modeling -- 4.2 Retrieving Basic Statistics -- 4.3 Graph Data Science Algorithms -- 5 Conclusion -- References -- Design and Compression Study for Convolutional Neural Networks Based on Evolutionary Optimization for Thoracic X-Ray Image Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 CNN Design -- 3.2 CNN Compression -- 4 Experiments -- 4.1 Expirement Configuration and Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- TF-MOPNAS: Training-free Multi-objective Pruning-Based Neural Architecture Search -- 1 Introduction -- 2 Background and Related Works -- 2.1 Progressive Search Space Shrinking and Architecture Selection -- 2.2 Training-free Metrics in Pruning-based NAS -- 3 Training-free Multi-objective Pruning-based Neural Architecture Search -- 4 Experiments and Results. 4.1 Results on NAS-Bench-101. |
| Record Nr. | UNINA-9910595027103321 |
Nguyen Ngoc Thanh (Computer scientist)
|
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| Cham : , : Springer International Publishing AG, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Computational Collective Intelligence : 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings
| Computational Collective Intelligence : 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28-30, 2022, Proceedings |
| Autore | Nguyen Ngoc Thanh (Computer scientist) |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2022 |
| Descrizione fisica | 1 online resource (863 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
ManolopoulosYannis
ChbeirRichard KozierkiewiczAdrianna TrawińskiBogdan |
| Collana | Lecture Notes in Computer Science |
| Soggetto non controllato | Science |
| ISBN | 3-031-16014-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Collective Intelligence and Collective Decision-Making -- Inferring Event Causality in Films via Common Knowledge Corpora -- 1 Introduction -- 1.1 Background -- 1.2 Research Objectives and Contributions -- 2 Background and Related Work -- 2.1 Event Causality in Films -- 2.2 Computational Methods -- 3 Event Causality Inference System -- 4 Evaluation Experiments -- 5 Discussion -- References -- Cooperation Game on Communication Multigraph with Fuzzy Parameters -- 1 Introduction -- 2 Preliminaries -- 3 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities -- 4 Concept of the FUZZY Value of the Sub-additive Cooperative Game on Communication Multigraph with Fuzzy Capacities and Fuzzy Goal -- 5 Conclusions and Future Works -- References -- Impact of Similarity Measure on the Quality of Communities Detected in Social Network by Hierarchical Clustering -- 1 Introduction -- 2 Organizational Social Networks -- 3 Community Detection Problem -- 4 Hierarchical Clustering Approach to the Community Detection -- 4.1 Hierarchical Clustering -- 4.2 Similarity Measures in Social Networks -- 4.3 Hierarchical Clustering Approach to the Community Detection in Organizational Social Network -- 5 Computational Experiment -- 6 Conclusions -- References -- An Approach to Modeling a Real-Time Updated Environment Based on Messages from Agents -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Environment Model Assumptions -- 3.2 Environment Model -- 4 Case Study: AriaDNA Life System -- 4.1 Space and Dimensions -- 4.2 Entity Types -- 4.3 Messages -- 4.4 Interactions -- 5 Summary -- References -- Updating the Result Ontology Integration at the Concept Level in the Event of the Evolution of Their Components -- 1 Introduction -- 2 Related Works.
3 Basic Notions -- 4 Methods of Updating Integrated Ontologies -- 4.1 Updating Results of Ontology Integration After Concept Removal -- 4.2 Updating Results of Ontology Integration After Adding New Concepts -- 4.3 Updating Results of Ontology Integration After Concepts Modification -- 5 Experimental Evaluation -- 6 Summary -- References -- Integrating Machine Learning into Learner Profiling for Adaptive and Gamified Learning System -- 1 Introduction -- 2 Proposed Method -- 2.1 Functional Architecture -- 3 Experimentation and Results -- 3.1 Used Method -- 3.2 Results -- 4 Conclusion -- References -- Deep Learning Techniques -- A New Deep Learning Fusion Approach for Emotion Recognition Based on Face and Text -- 1 Introduction -- 2 Facial-Textual Emotion Recognition (FTxER) Architecture -- 3 Results and Discussion -- 4 Conclusion and Future Work -- References -- Cycle Route Signs Detection Using Deep Learning -- 1 Introduction -- 2 Problem Definition -- 2.1 Car License Plate Recognition and Reading -- 2.2 Real-time Traffic Sign Detection Using CNN -- 3 Solution -- 3.1 YOLO -- 3.2 OCR -- 4 Implementation -- 4.1 Technologies Used -- 4.2 Data Training -- 4.3 Applications -- 5 Testing of Developed Application -- 6 Conclusion -- References -- Data Augmentation for Morphological Analysis of Histopathological Images Using Deep Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Morphing Concept -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework -- 4 Experiments and Results -- 4.1 Experimental Corpus -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusions and Further Work -- References. Deep Convolutional Neural Network for Arabic Speech Recognition -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 3.1 Convolutional Neural Network (CNN) -- 3.2 LSTM -- 4 Experimental Results and Discussion -- 4.1 Datasets and Input Features -- 4.2 Results and Discussion -- 5 Conclusion and Future Works -- References -- RingNet: Geometric Deep Representation Learning for 3D Multi-domain Protein Shape Retrieval -- 1 Introduction -- 2 Related Works -- 3 RingNet Neural Network -- 3.1 Descriptors Calculation -- 3.2 RingNet Layer -- 3.3 Fusion Layer -- 4 Experimental Results -- 4.1 Dataset and Metrics -- 4.2 3D Protein Shape Classification -- 4.3 3D Protein Shape Retrieval -- 5 Conclusion -- References -- Patch Selection for Melanoma Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description and Preprocessing -- 2.2 Entropy -- 2.3 Mean Exhaustive Minimum Distance (MEMD) Criterion -- 2.4 Network Architecture -- 3 Experimental Results -- 4 Conclusion -- References -- Natural Language Processing -- Multi-model Analysis of Language-Agnostic Sentiment Classification on MultiEmo Data -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Pipeline -- 3.2 MultiEmo Dataset -- 3.3 Scenarios -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tunisian Users on Social Networks: Overcoming the Challenge of Multilingual Comments in the Tunisian Dialect -- Abstract -- 1 Introduction -- 2 Specificities of Tunisian Dialect Sentiment Analysis -- 2.1 The Use of Multilingual Vocabulary -- 2.2 The Phenomenon of Linguistic Code-switching -- 3 Background and Related Work -- 3.1 Arabic Dialects Sentiment Analysis -- 3.2 Tunisian Dialect Sentiment Analysis -- 3.3 Discussion -- 4 Proposed Methodology -- 4.1 Data Collection -- 4.2 Data Preprocessing -- 4.3 Model Training. 5 Experiments and Results -- 5.1 Baseline -- 5.2 Dataset -- 5.3 Experimental Results -- 5.4 Discussion -- 6 Conclusion -- Acknowledgment -- References -- Non-Contextual vs Contextual Word Embeddings in Multiword Expressions Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 3.1 Non-Contextual Dataset -- 3.2 Contextual Dataset -- 4 Methods for Multiword Expression Detection -- 5 Experiments -- 6 Results -- 7 Discussion -- 8 Conclusions and Future Work -- References -- Context-free Transformer-based Generative Lemmatiser for Polish -- 1 Introduction -- 2 Related Works -- 2.1 Contextual Lemmatisation -- 2.2 Context-free Lemmatisation -- 2.3 Lemmatisation for the Polish Language -- 3 Problem Description -- 4 Architecture -- 4.1 Transformer-based Lemmatiser -- 5 Experiments -- 6 Conclusions -- References -- French Object Clitics in the Interlanguage: A Linguistic Description and a Formal Analysis in the ACCG Framework -- 1 Introduction -- 2 Interlanguage -- 2.1 The Concept -- 2.2 French Object Clitics in the Interlanguage -- 3 Applicative Combinatory Categorical Grammar -- 4 Object Clitics by Means of ACCG -- 5 Conclusion -- References -- Contradiction Detection Approach Based on Semantic Relations and Evidence of Uncertainty -- 1 Introduction -- 2 Related Work -- 3 Motivation and Proposed Model -- 3.1 Motivation -- 3.2 Description of the Approach -- 4 Semantic Construction of a Sentence -- 4.1 Pretreatment -- 4.2 Concept Extraction -- 4.3 Extraction of Uncertainty Expressions -- 4.4 Calculation of Degree of Uncertainty -- 4.5 Extraction of Binary Relations -- 5 Contradiction Assessment -- 5.1 Detection of Opposing Information -- 5.2 Detection of Contradiction -- 6 Experimental Evaluation -- 6.1 Test Environment -- 6.2 Experiments and Results -- 7 Conclusion -- References -- C-DESERT Score for Arabic Text Summary Evaluation -- Abstract. 1 Introduction -- 2 Related works -- 3 Proposed method -- 3.1 Document Embedding Model -- 3.2 Building the Doc2Vec Model -- 3.3 Features -- 3.4 Combination Scheme -- 4 Experiments -- 4.1 Data Sets -- 4.2 Result -- 5 Conclusion -- References -- Data Mining and Machine Learning -- Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models -- 1 Introduction -- 2 Related Works -- 3 Experiments -- 3.1 Datasets -- 3.2 Features -- 3.3 Methods -- 3.4 Tool Implementation -- 3.5 Experiment Scenarios -- 4 Results and Analysis -- 4.1 Experiment One -- 4.2 Experiment Two -- 4.3 Experiment Three -- 5 Conclusions -- References -- Simulation System for Producing Real World Dataset to Predict the Covid-19 Contamination Process⋆ -- 1 Introduction -- 2 Contact Tracing -- 2.1 GPS Accuracy -- 2.2 Bluetooth LE Distance Measurement -- 2.3 WiFi -- 2.4 Zigbee -- 2.5 Comparing Technologies -- 3 Dataset Collection -- 3.1 Simulating Real World Scene -- 4 Processing Data -- 4.1 Graph Modeling -- 4.2 Retrieving Basic Statistics -- 4.3 Graph Data Science Algorithms -- 5 Conclusion -- References -- Design and Compression Study for Convolutional Neural Networks Based on Evolutionary Optimization for Thoracic X-Ray Image Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 CNN Design -- 3.2 CNN Compression -- 4 Experiments -- 4.1 Expirement Configuration and Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- TF-MOPNAS: Training-free Multi-objective Pruning-Based Neural Architecture Search -- 1 Introduction -- 2 Background and Related Works -- 2.1 Progressive Search Space Shrinking and Architecture Selection -- 2.2 Training-free Metrics in Pruning-based NAS -- 3 Training-free Multi-objective Pruning-based Neural Architecture Search -- 4 Experiments and Results. 4.1 Results on NAS-Bench-101. |
| Record Nr. | UNISA-996490360703316 |
|
Nguyen Ngoc Thanh (Computer scientist)
|
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| Cham : , : Springer International Publishing AG, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational collective intelligence : 13th international conference, ICCCI 2021, Rhodes, Greece, September 29-October 1, 2021, proceedings / / edited by Ngoc Thanh Nguyen [and three others]
| Computational collective intelligence : 13th international conference, ICCCI 2021, Rhodes, Greece, September 29-October 1, 2021, proceedings / / edited by Ngoc Thanh Nguyen [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (817 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Expert systems (Computer science)
Intelligent agents (Computer software) |
| ISBN | 3-030-88081-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464488803316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Computational collective intelligence : 13th international conference, ICCCI 2021, Rhodes, Greece, September 29-October 1, 2021, proceedings / / edited by Ngoc Thanh Nguyen [and three others]
| Computational collective intelligence : 13th international conference, ICCCI 2021, Rhodes, Greece, September 29-October 1, 2021, proceedings / / edited by Ngoc Thanh Nguyen [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (817 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Expert systems (Computer science)
Intelligent agents (Computer software) |
| ISBN | 3-030-88081-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910502646603321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Collective Intelligence [[electronic resource] ] : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings / / edited by Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen
| Computational Collective Intelligence [[electronic resource] ] : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings / / edited by Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXIX, 905 p. 519 illus., 237 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Application software Image processing—Digital techniques Computer vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks |
| ISBN | 3-030-63007-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Knowledge Engineering and Semantic Web -- Social Networks and Recommender Systems -- Collective Decision-Making -- Applications of Collective Intelligence -- Data Mining Methods and Applications -- Machine Learning Methods -- Deep Learning and Applications for Industry 4.0 -- Computer Vision Techniques -- Biosensors and Biometric Techniques -- Innovations in Intelligent Systems -- Natural Language Processing -- Low Resource Languages Processing -- Computational Collective Intelligence and Natural Language Processing -- Computational Intelligence for Multimedia Understanding -- Intelligent Processing of Multimedia in Web Systems. |
| Record Nr. | UNINA-9910427668903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational Collective Intelligence [[electronic resource] ] : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings / / edited by Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen
| Computational Collective Intelligence [[electronic resource] ] : 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings / / edited by Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawiński, Gottfried Vossen |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XXIX, 905 p. 519 illus., 237 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Computer engineering Computer networks Application software Image processing—Digital techniques Computer vision Artificial Intelligence Computer Engineering and Networks Computer and Information Systems Applications Computer Imaging, Vision, Pattern Recognition and Graphics Computer Communication Networks |
| ISBN | 3-030-63007-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Knowledge Engineering and Semantic Web -- Social Networks and Recommender Systems -- Collective Decision-Making -- Applications of Collective Intelligence -- Data Mining Methods and Applications -- Machine Learning Methods -- Deep Learning and Applications for Industry 4.0 -- Computer Vision Techniques -- Biosensors and Biometric Techniques -- Innovations in Intelligent Systems -- Natural Language Processing -- Low Resource Languages Processing -- Computational Collective Intelligence and Natural Language Processing -- Computational Intelligence for Multimedia Understanding -- Intelligent Processing of Multimedia in Web Systems. |
| Record Nr. | UNISA-996418223903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| 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 |
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Nguyen Ngoc Thanh (Computer scientist)
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| Cham : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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