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

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An Empirical Analysis of?Data Reduction Techniques for?k-NN Classificationd Prototype Generation (PG) methods. The research provides an in-depth examination of these methodologies, categorizing DRTs into two primary categories: PS and PG, and further dividing them into three sub-categories: condensation methods, edition methods, and hybrid methods. An experimental study c

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Controlling Popularity Bias in?Sequential Recommendation Models result, through the way that people consume news and media, we are transitioning from a static media delivery model to a dynamic, personalized system which many are adapting and even enjoying the resulted changes. Personalized recommendations are mostly made with the help of the machine learning mo

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Enhanced Item Recommendation via?Graph Properties in?Sparse Data and analytical perspectives. The latest works focus on ranking-based personalized recommenders. However, they recommend the same number of items for everyone and still suffer from the interaction sparsity issue. We propose a complex-graph-oriented supervised learning-based link prediction with a re

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Modeling the Air Conditioner Performance Tests Using Artificial Neural Network Simulator (ANNS-AC)oses. This helps save time and effort instead of repeating the test for validation. A backpropagation ANN models with multiple hidden layers were trained using 22 input variables and three targets. More than 800 test reports were used to train the ANN model. The input processing functions, neuron si

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A Voting Approach for?Explainable Classification with?Rule Learning instance. Contrarily, in this paper, we investigate the application of rule learning methods in such a context. Thus, classifications become based on comprehensible (first-order) rules, explaining the predictions made. In general, however, rule-based classifications are less accurate than state-of-

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Dynamic Stacking Optimization in?Unpredictable Environments: A Focus on?Crane SchedulingIt entails the utilization of cranes to relocate products, with the relocation needing to be scheduled while adhering to various time constraints. This paper addresses the challenge of developing solution approaches for such dynamic stacking problems in uncertain environments, particularly the envir

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FASTER-CE: Fast, Sparse, Transparent, and?Robust Counterfactual Explanationsal explanation definition, researchers have also identified other desirable properties that make counterfactual explanations more usable on the deployment and the end-user sides: speed of explanation generation, robustness/sensitivity, and succinctness of explanations. Motivated by the need to make

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Optimizations for?Learning from?Linear Feedback Shift Register Variations with?Artificial Neural Netest results have been obtained for learning on Linear Feedback Shift Registers (LFSRs). Due to the deterministic nature of LFSRs, Decision Trees (DTs) and Artificial Neural Networks (ANNs) were able to reach up to . test accuracy for next bit prediction tasks. Despite important advances, a number of

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