Foundations of Intelligent Systems

Table of Contents

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   Book Overview
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   Chapter 1 Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles
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   Chapter 2 Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models
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   Chapter 3 Disruptive Event Identification in Online Social Network
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   Chapter 4 Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias
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   Chapter 5 Sarcasm Detection in Tunisian Social Media Comments: Case of COVID-19
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   Chapter 6 Multimodal Deep Learning and Fast Retrieval for Recommendation
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   Chapter 7 Mining News Articles Dealing with Food Security
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   Chapter 8 Identification of Paragraph Regularities in Legal Judgements Through Clustering and Textual Embedding
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    Chapter 9 Aspect Term Extraction Improvement Based on a Hybrid Method
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    Chapter 10 Exploring the Impact of Gender Bias Mitigation Approaches on a Downstream Classification Task
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    Chapter 11 A Semi-automatic Data Generator for Query Answering
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    Chapter 12 XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity
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    Chapter 13 Impact of Feedback Type on Explanatory Interactive Learning
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    Chapter 14 Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content
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    Chapter 15 An Interpretable Machine Learning Approach to Prioritizing Factors Contributing to Clinician Burnout
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    Chapter 16 A General-Purpose Method for Applying Explainable AI for Anomaly Detection
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    Chapter 17 More Sanity Checks for Saliency Maps
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    Chapter 18 Deep Reinforcement Learning for Automated Stock Trading: Inclusion of Short Selling
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    Chapter 19 Scaling Posterior Distributions over Differently-Curated Datasets: A Bayesian-Neural-Networks Methodology
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    Chapter 20 Ensembling Sparse Autoencoders for Network Covert Channel Detection in IoT Ecosystems
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    Chapter 21 Towards Automation of Pollen Monitoring: Image-Based Tree Pollen Recognition
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    Chapter 22 Rough Sets for Intelligence on Embedded Systems
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    Chapter 23 Context as a Distance Function in ConSQL
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    Chapter 24 Detecting Anomalies with  $${{\textrm{Latent}}Out}$$ Latent O u t : Novel Scores, Architectures, and Settings
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    Chapter 25 Richness Fallacy
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    Chapter 26 Adapting Loss Functions to Learning Progress Improves Accuracy of Classification in Neural Networks
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    Chapter 27 Multiscale and Multivariate Time Series Clustering: A New Approach
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    Chapter 28 Improve Calibration Robustness of Temperature Scaling by Penalizing Output Entropy
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    Chapter 29 Understanding Negative Calibration from Entropy Perspective
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    Chapter 30 A New Clustering Preserving Transformation for  k -Means Algorithm Output
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    Chapter 31 A Transformer-Based Framework for Geomagnetic Activity Prediction
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    Chapter 32 AS-SIM: An Approach to Action-State Process Model Discovery
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    Chapter 33 Combining Active Learning and Fast DNN Ensembles for Process Deviance Discovery
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    Chapter 34 Temporal Graph-Based CNNs (TG-CNNs) for Online Course Dropout Prediction
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    Chapter 35 Graph Convolutional Networks Using Node Addition and Edge Reweighting
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    Chapter 36 Audio Super-Resolution via Vision Transformer
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    Chapter 37 Similarity Embedded Temporal Transformers: Enhancing Stock Predictions with Historically Similar Trends
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    Chapter 38 Investigating Noise Interference on Speech Towards Applying the Lombard Effect Automatically
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    Chapter 39 Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers
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    Chapter 40 Towards Tailored Intervention in Medicine Using Patients’ Segmentation
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    Chapter 41 Application of Association Rules to Classify IBD Patients
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    Chapter 42 Unsupervised Learning Based Rule Generating System with Temporal Features Extractions Tuned for Tinnitus Retraining Therapy
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    Chapter 43 TrueDetective 4.0: A Big Data Architecture for Real Time Anomaly Detection
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    Chapter 44 Optimising the Machine Translation Workflow
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    Chapter 45 A Comparative Study: Classification Vs. Matrix Factorization for Therapeutics Recommendation
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    Chapter 46 Document Layout Analysis with Variational Autoencoders: An Industrial Application