Info
Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras
| Complex Pattern Mining : New Challenges, Methods and Applications / / edited by Annalisa Appice, Michelangelo Ceci, Corrado Loglisci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (x, 250 pages) : illustrations |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Pattern recognition systems Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Automated Pattern Recognition |
| ISBN | 3-030-36617-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Efficient Infrequent Pattern Mining using Negative Itemset Tree -- Hierarchical Adversarial Training for Multi-Domain -- Optimizing C-index via Gradient Boosting in Medical Survival Analysis -- Order-preserving Biclustering Based on FCA and Pattern Structures -- A text-based regression approach to predict bug-fix time -- A Named Entity Recognition Approach for Albanian Using Deep Learning -- A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining -- Efficient Declarative-based Process Mining using an Enhanced Framework -- Exploiting Pattern Set Dissimilarity for Detecting Changes in Communication Networks -- Classification and Clustering of Emotive Microblogs in Albanian: Two User-Oriented Tasks. |
| Record Nr. | UNINA-9910484953003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Libraries: The Era of Big Data and Data Science [[electronic resource] ] : 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings / / edited by Michelangelo Ceci, Stefano Ferilli, Antonella Poggi
| Digital Libraries: The Era of Big Data and Data Science [[electronic resource] ] : 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings / / edited by Michelangelo Ceci, Stefano Ferilli, Antonella Poggi |
| Autore | Ceci Michelangelo |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 189 p. 73 illus., 62 illus. in color.) |
| Disciplina | 025.04 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Information storage and retrieval
Artificial intelligence Application software User interfaces (Computer systems) Database management Education—Data processing Information Storage and Retrieval Artificial Intelligence Computer Appl. in Social and Behavioral Sciences User Interfaces and Human Computer Interaction Database Management Computers and Education |
| Soggetto non controllato | Digital libraries |
| ISBN | 3-030-39905-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Information Retrieval -- Bid Data and Data Science in DL -- Cultural Heritage -- Open Science. . |
| Record Nr. | UNISA-996465347603316 |
Ceci Michelangelo
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Digital Libraries: The Era of Big Data and Data Science : 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings / / edited by Michelangelo Ceci, Stefano Ferilli, Antonella Poggi
| Digital Libraries: The Era of Big Data and Data Science : 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings / / edited by Michelangelo Ceci, Stefano Ferilli, Antonella Poggi |
| Autore | Ceci Michelangelo |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XIV, 189 p. 73 illus., 62 illus. in color.) |
| Disciplina | 025.04 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Information storage and retrieval
Information organization Information retrieval Artificial intelligence Application software User interfaces (Computer systems) Database management Education - Data processing Information Storage and Retrieval Artificial Intelligence Computer Appl. in Social and Behavioral Sciences User Interfaces and Human Computer Interaction Database Management Computers and Education |
| ISBN | 3-030-39905-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Information Retrieval -- Bid Data and Data Science in DL -- Cultural Heritage -- Open Science. . |
| Record Nr. | UNINA-9910373925403321 |
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Ceci Michelangelo
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Discovery Science [[electronic resource] ] : 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos, Michelangelo Ceci
| Discovery Science [[electronic resource] ] : 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos, Michelangelo Ceci |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXI, 482 p. 137 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Application software Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-030-01771-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification -- Meta-Learning -- Reinforcement Learning -- Streams and Time Series -- Subgroup and Subgraph Discovery -- Text Mining -- Applications. |
| Record Nr. | UNISA-996466451503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Discovery Science : 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos, Michelangelo Ceci
| Discovery Science : 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos, Michelangelo Ceci |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXI, 482 p. 137 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Application software Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Computer Appl. in Social and Behavioral Sciences |
| ISBN |
9783030017712
3030017710 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Classification -- Meta-Learning -- Reinforcement Learning -- Streams and Time Series -- Subgroup and Subgraph Discovery -- Text Mining -- Applications. |
| Record Nr. | UNINA-9910349397603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Discovery Science [[electronic resource] ] : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings / / edited by Toon Calders, Michelangelo Ceci, Donato Malerba
| Discovery Science [[electronic resource] ] : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings / / edited by Toon Calders, Michelangelo Ceci, Donato Malerba |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXI, 492 p. 131 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval Database management Algorithms Pattern recognition Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Database Management Algorithm Analysis and Problem Complexity Pattern Recognition |
| ISBN | 3-319-46307-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern mining and rules -- Structured output prediction -- Applications -- Ensemble Learning -- Classification -- Networks -- Kernels and deep learning. . |
| Record Nr. | UNISA-996465273403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Discovery Science : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings / / edited by Toon Calders, Michelangelo Ceci, Donato Malerba
| Discovery Science : 19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings / / edited by Toon Calders, Michelangelo Ceci, Donato Malerba |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XXI, 492 p. 131 illus.) |
| Disciplina | 501 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Information storage and retrieval systems Database management Algorithms Pattern recognition systems Artificial Intelligence Data Mining and Knowledge Discovery Information Storage and Retrieval Database Management Automated Pattern Recognition |
| ISBN | 3-319-46307-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern mining and rules -- Structured output prediction -- Applications -- Ensemble Learning -- Classification -- Networks -- Kernels and deep learning. . |
| Record Nr. | UNINA-9910484897403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Foundations of intelligent systems : 26th international symposium, ISMIS 2022, Cosenza, Italy, October 3-5, 2022, proceedings / / edited by Michelangelo Ceci [and four others]
| Foundations of intelligent systems : 26th international symposium, ISMIS 2022, Cosenza, Italy, October 3-5, 2022, proceedings / / edited by Michelangelo Ceci [and four others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (497 pages) |
| Disciplina | 060 |
| Collana | Lecture Notes in Computer Science Ser. |
| Soggetto topico | Artificial intelligence |
| ISBN | 3-031-16564-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks -- Adaptive Machine Learning for Data Streams -- Interactive Machine Learning -- Contents -- Social Media and Recommendation -- Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles -- 1 Introduction -- 2 Related Work -- 2.1 Classifiers -- 2.2 Ensembles -- 3 Ensemble Approach and Evaluation -- 3.1 Experimental Setup -- 3.2 Analysis of Individual Component Approaches -- 3.3 Analysis of Ensemble Approaches -- 4 Results and Discussion -- 5 Conclusions and Future Work -- References -- Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models -- 1 Introduction -- 2 Related Work -- 3 Design and Research Methodology -- 4 Experimental Settings -- 4.1 Dataset -- 4.2 Conventional Machine Learning Models -- 4.3 Deep Neural Networks -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- Disruptive Event Identification in Online Social Network -- 1 Introduction -- 2 Disruptive Event Identification Framework -- 2.1 Data Acquisition and Preprocessing -- 2.2 Clustering and Event Identification -- 3 Result and Discussion -- 3.1 Dataset Used -- 3.2 Performance Metrics Used -- 4 Conclusion -- References -- Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 News Domains Data -- 3.2 Social Media Posts -- 4 Methodology -- 4.1 Type-1: News Domain Labeling -- 4.2 Type-2: Sentiment Labeling -- 5 Results -- 5.1 Political Leaning Labeling -- 5.2 Methods for Learning Text Representations -- 5.3 Political Leaning Prediction -- 6 Conclusion and Future Work -- References -- Sarcasm Detection in Tunisian Social Media Comments: Case of COVID-19 -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 2.2 Tunisian Dialect -- 3 Dataset.
3.1 Data Collection and Preprocessing -- 3.2 Data Annotation -- 4 Experimental Results -- 5 Conclusion -- References -- Multimodal Deep Learning and Fast Retrieval for Recommendation -- 1 Introduction -- 2 Multimodal Embedding of Text and Images -- 3 Search and Retrieval Through LSH -- 4 Experimental Analysis -- References -- Natural Language Processing -- Mining News Articles Dealing with Food Security -- 1 Introduction -- 2 Proposed Approach -- 2.1 Text Mining Approaches -- 2.2 Our Food Security Pipeline -- 3 Experiments -- 3.1 Corpus of Newspapers -- 3.2 Results -- 4 Conclusion and Future Work -- References -- Identification of Paragraph Regularities in Legal Judgements Through Clustering and Textual Embedding -- 1 Introduction -- 2 The Proposed Method JPReg -- 2.1 Training of Document and Paragraph Embedding Models -- 2.2 Embedding of the Paragraph of the Reference Set -- 2.3 Identification of Paragraph Regularities -- 3 Experiments -- 3.1 Results -- 4 Conclusions -- References -- Aspect Term Extraction Improvement Based on a Hybrid Method -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Step 1: Linguistic Knowledge-Based Method for Aspect Terms Extraction -- 3.2 Step 2: Deep Learning-Based Method for Aspect Terms Extraction -- 3.3 Step 3: Improvement of the Aspect Final List with an Out-Domain Pruning Algorithm -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Evaluation of Our Proposed Method for Aspect Terms Extraction -- 5 Conclusion -- References -- Exploring the Impact of Gender Bias Mitigation Approaches on a Downstream Classification Task -- 1 Introduction -- 2 Related Work -- 3 Approach -- 4 Results -- 5 Conclusion -- References -- A Semi-automatic Data Generator for Query Answering -- 1 Introduction -- 2 Proposed Method -- 3 Experiments -- 3.1 Quantitative Analysis -- 3.2 A Better Estimation for QA Model Performances. 4 Conclusion -- References -- Explainability -- XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 4 Empirical Evaluation -- 4.1 Dataset Description -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusion -- References -- Impact of Feedback Type on Explanatory Interactive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Model Training -- 2.2 Model Explanation -- 2.3 Feedback Collection -- 3 Methods -- 3.1 Dataset for XIL -- 3.2 Experimental Setup -- 3.3 Model Training -- 4 Results -- 4.1 Random Decoy Fashion MNIST -- 4.2 Class-wise Decoy Fashion MNIST -- 5 Conclusion -- References -- Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content -- 1 Introduction -- 2 Related Work -- 3 Framework -- 3.1 Hierarchical Deep Multi-label Classification -- 3.2 From Prediction to Explanation -- 4 Experimental Results -- 4.1 Numerical Results -- 4.2 Explanation Prototypes -- 5 Conclusions and Future Work -- References -- An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout -- 1 Introduction -- 2 Methods -- 2.1 Data Collection and Study Measures -- 2.2 Dataset Preparation -- 2.3 Feature Selection -- 2.4 Classification -- 2.5 Feature Importance -- 3 Results -- 3.1 Feature Selection -- 3.2 Classification -- 3.3 Feature Importance -- 4 Discussion -- 5 Conclusion -- References -- A General-Purpose Method for Applying Explainable AI for Anomaly Detection -- 1 Anomaly Detection and Interpretability -- 2 Conventions and Basic Definitions -- 3 Explainability vs. Interpretability -- 3.1 Explainability -- 3.2 Interpretablity -- 4 An Approach for Interpreting Anomalies -- 4.1 Choose an Exemplar Baseline Set -- 4.2 Choose a Measure of Dissimilarity -- 4.3 Select a Baseline Point. 4.4 Choose a Path from the Anomaly to the Baseline -- 4.5 Apply an Explanation Function with the Baseline -- 5 Experiments -- 6 Discussion and Future Work -- References -- More Sanity Checks for Saliency Maps -- 1 Introduction -- 2 Related Work -- 3 Comparing Human's Saliency Maps to XAI's -- 4 Dispersion for Saliency Maps -- 5 Conclusion -- References -- Intelligent Systems -- Deep Reinforcement Learning for Automated Stock Trading: Inclusion of Short Selling -- 1 Introduction -- 2 Related Works -- 3 Background -- 3.1 Deep Reinforcement Learning Algorithms -- 3.2 Technical Indicators -- 4 Deep Reinforcement Learning Approach to Automated Stock Trading -- 4.1 Stock Trading Problem Formulation -- 4.2 Dataset -- 4.3 Trading Objectives -- 4.4 Financial Markets' Constraints -- 4.5 Shorting Thresholding -- 4.6 Turbulence as a Safety Switch -- 5 Results and Discussion -- 5.1 Performance Metrics -- 5.2 Performance Evaluation of Modified DRL Agents -- 6 Conclusion -- References -- Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology -- 1 Introduction -- 2 Case Study: Stock Quotation Prediction -- 3 Related Work -- 4 Innovative Sampling Methods -- 5 Conclusions and Future Work -- References -- Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems -- 1 Introduction -- 2 Attack Scenario and Threat Modelling -- 2.1 Attack Scenario -- 2.2 Dataset for Modelling the Covert Channel -- 3 Deep Ensemble Learning Scheme -- 3.1 Detection Through a Single Autoencoder -- 3.2 Learning and Combining Different Detectors -- 4 Performance Evaluation -- 4.1 Pre-processing, Parameters and Evaluation Metrics -- 4.2 Numerical Results -- 5 Conclusions and Future Work -- References -- Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition -- 1 Introduction -- 1.1 Background. 2 Biological Data -- 3 Methods -- 3.1 Object Detection -- 3.2 Quality Measures for Model Evaluation -- 3.3 Training Process -- 3.4 Statistical Analysis -- 4 Results -- 5 Summary and Conclusions -- References -- Rough Sets for Intelligence on Embedded Systems -- 1 Introduction -- 1.1 Embedded Systems -- 2 Algorithm Preliminaries -- 2.1 KNN -- 2.2 FRNN -- 2.3 FROVOCO -- 3 Experiments -- 3.1 Model Training -- 3.2 Model Testing -- 4 Future Research -- 5 Conclusion -- References -- Context as a Distance Function in ConSQL -- 1 Introduction -- 2 Context as a First-Class Citizen for Query Answering -- 2.1 The Concept of Context in a Conversation -- 2.2 Cumulative and Disjunctive Context Switch -- 3 Contextual Query Language ConSQL -- 3.1 Syntax of ConSQL -- 3.2 Semantics of ConSQL -- 4 Conclusions -- References -- Classification and Clustering -- Detecting Anomalies with LatentOut: Novel Scores, Architectures, and Settings -- 1 Introduction -- 2 Preliminaries and Method -- 2.1 Definition of LatentOut -- 2.2 Extension to GAAL Architectures -- 2.3 LatentOut for Semi-supervised Outlier Detection -- 3 Experimental Results -- 4 Conclusion -- References -- Richness Fallacy -- 1 Introduction -- 2 Richness and Learnability -- 3 Richness Problems in Euclidean Space -- 4 Disturbing Effects of Consistency Axiom on Richness Axiom -- 5 Super-Ball Separation -- 6 Final Remarks -- References -- Adapting Loss Functions to Learning Progress Improves Accuracy of Classification in Neural Networks -- 1 Introduction -- 2 Models and Methods -- 2.1 Neural Network Models and Data Sets -- 2.2 Power Error Loss Function (PEL) -- 2.3 Hyperparameter Optimization -- 3 Results -- 4 Summary and Conclusions -- References -- Multiscale and Multivariate Time Series Clustering: A New Approach -- 1 Introduction -- 2 Notations and Definitions. 3 Multiscale and Multivariate Time Series Clustering. |
| Record Nr. | UNISA-996490355003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Foundations of Intelligent Systems : 26th International Symposium, ISMIS 2022, Cosenza, Italy, October 3–5, 2022, Proceedings / / edited by Michelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, Zbigniew W. Raś
| Foundations of Intelligent Systems : 26th International Symposium, ISMIS 2022, Cosenza, Italy, October 3–5, 2022, Proceedings / / edited by Michelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, Zbigniew W. Raś |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (497 pages) |
| Disciplina | 060 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Data mining Social sciences—Data processing Computer vision Artificial Intelligence Computer and Information Systems Applications Data Mining and Knowledge Discovery Computer Application in Social and Behavioral Sciences Computer Vision Sistemes experts (Informàtica) Agents intel·ligents (Programari) Intel·ligència artificial Mineria de dades |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031165641
3031165640 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Social Media and Recommendation -- Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles -- Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models -- Disruptive Event Identification in Online Social Network -- Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias -- Sarcasm detection in Tunisian social media comments: Case of COVID-19 -- Multimodal Deep Learning and Fast Retrieval for Recommendation -- Natural Language Processing -- Mining news articles dealing with Food Security -- Identification of Paragraph Regularities in Legal Judgements through Clustering and Textual Embedding -- Aspect term extraction improvement based on a hybrid method -- Exploring the Impact of Gender Bias Mitigation Approaches on a Downstream Classification Task -- A semi-automatic data generator for Query Answering -- Explainability, -- XAI to explore robustness of features in adversarial training for cybersecurity -- Impact of Feedback Type on Explanatory Interactive Learning -- Learning and Explanation of Extreme Multi-Label Deep Classification Models for Media Content -- An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout -- A general-purpose method for applying Explainable AI for Anomaly Detection -- More Sanity Checks for Saliency Maps -- Intelligent Systems -- Deep Reinforcement Learning for Automated Stock Trading: Inclusion of Short Selling -- Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology -- Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems -- Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition -- Rough Sets for Intelligence on Embedded Systems -- Context as a Distance Function in ConSQL -- Classification and Clustering -- Detecting Anomalies with LatentOut: Novel Scores, Architectures, and Settings -- Richness Fallacy -- Adapting loss functions to learning progress improves accuracy of classification in neural networks -- Multiscale and multivariate time series clustering: A new approach -- Improve Calibration Robustness of Temperature Scaling by Penalizing Output Entropy -- Understanding Negative Calibration from Entropy Perspective -- A New Clustering Preserving Transformation for $k$-Means Algorithm Output -- Complex Data -- A Transformer-Based Framework for Geomagnetic Activity Prediction -- AS-SIM: an approach to Action-State Process Model Discovery -- Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery -- Temporal Graph-based CNNs (TG-CNNs) for Online Course Dropout Prediction -- Graph Convolutional Networks Using Node Addition and Edge Reweighting -- Audio Super-Resolution via Vision Transformer -- Similarity embedded temporal Transformers: Enhancing stock predictions with historically similar trends -- Investigating noise interference on speech towards applying the Lombard effect automatically -- Medical Applications -- Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers -- Towards Tailored Intervention in Medicine Using Patients’ Segmentation -- Application of association rules to classify IBD patients -- Unsupervised Learning Based Rule Generating System with Temporal Features Extractions Tuned for Tinnitus Retraining Therapy -- Industrial Applications -- TrueDetective 4.0: a Big data architetture for real time anomaly detection -- Optimising the Machine Translation Workflow: Analysis, Development, Benchmarking, Testing and Maintenance -- Classification vs Recommendation methods for Therapeutics Recommendation -- Document Layout Analysis with Variational Autoencoders : an Industrial Application. |
| Record Nr. | UNINA-9910595035103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Foundations of Intelligent Systems [[electronic resource] ] : 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Michelangelo Ceci, Nathalie Japkowicz, Jiming Liu, George A. Papadopoulos, Zbigniew W. Raś
| Foundations of Intelligent Systems [[electronic resource] ] : 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings / / edited by Michelangelo Ceci, Nathalie Japkowicz, Jiming Liu, George A. Papadopoulos, Zbigniew W. Raś |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XXV, 464 p. 111 illus.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Data mining Application software Optical data processing Artificial Intelligence Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Image Processing and Computer Vision |
| ISBN | 3-030-01851-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Bioinformatics and Health Informatics -- Graph Mining -- Image Analysis -- Intelligent Systems -- Mining Complex Patterns -- Novelty Detection and Class Imbalance -- Social Data Analysis -- Spatio-temporal Analysis -- Granular and Soft Clustering -- Topic Modelling and Opinion Mining. |
| Record Nr. | UNISA-996466453803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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