Titlebook: Artificial Intelligence Applications and Innovations; 17th IFIP WG 12.5 In Ilias Maglogiannis,John Macintyre,Lazaros Iliadis Conference pro

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The AI4Media Project: Use of Next-Generation Artificial Intelligence Technologies for Media Sector A raises significant questions in terms of trust, ethics, and accountability. This paper offers an introduction to the AI4Media project, which aims to build on recent advances of AI in order to offer innovative tools to the media sector. AI4Media unifies the fragmented landscape of media-related AI t

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An Automated Machine Learning Approach for Predicting Chemical Laboratory Material Consumptiony tests are executed at the Analytical Laboratories (AL), which receive production samples and execute several instrumental analyses. In order to improve the AL stock warehouse management, a Machine Learning (ML) project was developed, aiming to estimate the AL materials consumption based on week pl

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Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizesutomaton (OMA) and its proposed enhancements, which include the Pursuit and Transitivity phenomena, can solve problems with equi-sized partitions. Currently, these solutions also include one where the partition sizes possess a Greatest Common Divisor (GCD). In this paper, we propose an OMA-based sol

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Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportating city centers with main mass transit systems. The development of technologies that can provide a sense of security to the passenger when the driver is missing remains a challenging task. Moreover, such technologies are forced to adopt to the new reality formed by the COVID-19 pandemic, as it has

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Evaluating Task-General Resilience Mechanisms in a Multi-robot Team Taskn order to function properly in the long term. We thus isolate a set of perturbations that agents ought to address and demonstrate how task-agnostic perturbation detection and mitigation mechanisms can be integrated into a cognitive robotic architecture. We present results from experimental evaluati

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Lai-Chong Law,Ka-Ming Patrick Wongt classifier among linear SVM, Radial Basis Function Kernel SVM and random forest and their optimal parameters to predict the vital status of patients in different time windows based on a large cohort of patients’ gene expression data. The results are very encouraging in performance metrics compared

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