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.

Format:

Book

Contributor:

Ceci, Michelangelo, Editor.

Flesca, Sergio., Editor.

Masciari, Elio, Editor.

Manco, Giuseppe, Editor.

Raś, Zbigniew W., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 13515

Lecture Notes in Artificial Intelligence, 2945-9141 ; 13515

Language:

English

Subjects (All):

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.

Local Subjects:

Artificial Intelligence.

Computer and Information Systems Applications.

Data Mining and Knowledge Discovery.

Computer Application in Social and Behavioral Sciences.

Computer Vision.

Physical Description:

1 online resource (XVIII, 488 pages) : 152 illustrations, 112 illustrations in color.

Edition:

1st edition 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

This book constitutes the proceedings of the 26th International Symposium on Foundations of Intelligent Systems, ISMIS 2022, held in Cosenza, Italy, in October 2022. The 31 regular papers, 11 short papers and 4 industrial papers presented in this volume were carefully reviewed and selected from 71 submissions. They were organized in topical sections as follows: Social Media and Recommendation; Natural Language Processing; Explainability; Intelligent Systems; Classification and Clustering; Complex Data; Medical Applications; Industrial Applications. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-031-16564-1

9783031165641

Access Restriction:

Restricted for use by site license.