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 LayoutAnalysis with Variational Autoencoders : an Industrial Application.

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. .