Machine Learning and Knowledge Extraction -

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Résumé :
This book constitutes the refereed proceedings of the 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, held in virtually in August 2021. The 20 full papers and 2 short papers presented were carefully reviewed and selected from 48 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.

Sommaire:
Digital Transformation for Sustainable Development Goals (SDGs) - a Security, Safety and Privacy Perspective on AI.- When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised.- Self-Propagating Malware Containment via Reinforcement Learning.- Text2PyCode : Machine Translation of Natural Language Intent to Python Source Code.- Automated Short Answer Grading using Deep Learning : A Survey.- Fair and Adequate Explanations.- Mining Causal Hypotheses in Categorical Time Series by Iterating on Binary Correlations.- Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples.- Rice seed image-to-image translation using Generative Adversarial Networks to improve weedy rice image classification.- Reliable AI through SVDD and rule extraction.- Airbnb Price Prediction Using Machine Learning and Sentiment Analysis.- Towards Financial Sentiment Analysis in a South African Landscape.- Decisions are not all equal. Introducing a utility metric based on the case-wise raters' perceptions.- Deep Convolutional Neural Network(CNN) design for pathology detection of COVID-19 in chest X-Ray Images.- Anomaly detection for skin lesion images using replicator neural networks.- On the overlap between Grad-CAM saliency maps and explainable visual features in skin cancer images.- From Explainable to Reliable Artificial Intelligence.- Explanatory Pluralism in Explainable AI.- On the Trustworthiness of Tree Ensemble Explainability Methods.- Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoods.- MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers.- Transparent Ensembles for Covid-19 Prognosis.