Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Irena Koprinska,Paolo Mignone,Sepide

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Areeba Umair,Elio Masciari,Giusi Madeo,Muhammad Habib UllahTransformer-based architecture for both layout and content detection. This process and dataset represent a first step to automatically analyze handwritten and historical index tables. In addition to this paper and the PARES [.] dataset of historical index tables of 250 images, we release segmentatio

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1865-0929 Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022.?.The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 p

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A Social Media Tool for?Domain-Specific Information Retrieval - A Case Study in?Human Traffickingndexing the information gathered. The tool developed based on this approach was tested for a case study in the domain of Human Trafficking, more specifically in sexual exploitation, showing promising results and potential to be applied in a real-world scenario.

TATE

Fault Detection in?Wastewater Treatment Plants: Application of?Autoencoders Models with?Streaming Dasidered (drift, bias, precision degradation, spike and stuck) in three different scenarios with variations in the appearance order, intensity and duration of the faults. The best performance, considering different model configurations, was achieved by Convolutional-AE.

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Multi-modal Terminology Managementg a Terminology Management System (TMS) such as TermStar, which can make use of parallel corpora and collaboration functions to streamline the entire process, from terminological extraction to glossary approval and maintenance.

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Rules, Subgroups and?Redescriptions as?Features in?Classification Tasksretable features with in-depth knowledge about the studied domain problem. The performed results show that DAFNE is capable of producing provably useful features that increase overall predictive performance of different classification algorithms on a set of different classification datasets.