Titlebook: Artificial Intelligence Applications and Innovations; 20th IFIP WG 12.5 In Ilias Maglogiannis,Lazaros Iliadis,Antonios Papale Conference pr

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Hybrid Explanatory Interactive Machine Learning for Medical Diagnosisrnal mechanism. Using model-agnostic explanatory interactive ML (XIML), physicians iteratively train a ML model and revise its decision-making mechanism depicted as local explanation. Counterexamples serve as additional training data and statistically outweigh the human feedback. Unfortunately, coun

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IRfold: An RNA Secondary Structure Prediction Approachits primary structure. The prediction of an RNA’s secondary structure from its primary structure involves predicting which of its bases are most likely to pair. Computing the likelihood of each pair to obtain the set of pairings with the highest cumulative probability is computationally intractable.

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Machine Learning Models for Predicting Celiac Disease Based on Non-invasive Clinical Symptomsprotocols are being explored for this purpose. In this paper, we aim to use artificial intelligence models to support the medical diagnosis of Celiac Disease from clinical symptoms and biomarkers. Through our experiments, we identified accurate models able to predict Celiac Disease diagnosis from no

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Modeling Distributed and Flexible PHM Framework Based on the Belief Function Theoryach to decision-making under conditions of uncertainty and incomplete information. Central to our methodology is the modeling of beliefs and uncertainties through belief mass functions, enabling the representation and aggregation of diverse information source. This approach is particularly advantage

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MTA-Net: A Multi-task Assisted Network for Whole-Body Lymphoma Segmentationomography (CT) is widely used for lymphoma segmentation. Many methods do automatic segmentation of lymphoma based on PET/CT. However, a significant challenge that limits the effectiveness of the segmentation method is the large and imbalance variation in size of whole-body lymphoma lesions. For exam

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Revisiting the Problem of Missing Values in High-Dimensional Data and Feature Selection Effectow a listwise deletion in the presence of missingness leading to information loss. To address missingness, numerous imputation methods (IMs) have been proposed. Nonetheless, the choice of method is of key importance both in relation to computational cost, especially for high dimensional data, as wel

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Semantic Modelling for Representation and Integration of Health Data from Wearable Devicesr to achieve this, a seamless exchange of health information across different frameworks is needed, promoting interoperability among health data formats, a key factor in the progression of digital health solutions. In this paper, we present our work in defining and implementing a mapping between the

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The Role of Epigenetics in OCD: A Multi-order Adaptive Network Model for DNA-Methylation Pathways an obsessions, and repetitive behaviours and mental acts, to be called compulsions. Recent studies have linked OCD to increased methylation of the oxytocin receptor (OXTR) gene which potentially impacts OCD-linked social and emotional behaviours. Options to treat OCD include cognitive-behavioural ther