Artificial intelligence applications and innovations : 19th IFIP WG 12.5 International Conference, AIAI 2023, Len̤, Spain, June 14-17, 2023, Proceedings. Part II / Ilias Maglogiannis, Lazaros Iliadis, John MacIntyre, Manuel Dominguez, editors.

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Artificial intelligence applications and innovations : 19th IFIP WG 12.5 International Conference, AIAI 2023, Len̤, Spain, June 14-17, 2023, Proceedings. Part II / Ilias Maglogiannis, Lazaros Iliadis, John MacIntyre, Manuel Dominguez, editors.

9783031341076 (electronic bk.)
3031341074 (electronic bk.)
9783031341069

Cham : Springer, 2023.

English

1 online resource (xxxvii, 582 pages) : illustrations (some color).

10.1007/978-3-031-34107-6 doi

Q334 .I35 2023eb

006.3

This two-volume set of IFIP-AICT 675 and 676 constitutes the refereed proceedings of the 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, held in Len̤, Spain, during June 14-17, 2023. This event was held in hybrid mode. The 75 regular papers and 17 short papers presented in this two-volume set were carefully reviewed and selected from 185 submissions. The papers cover the following topics: Deep Learning (Reinforcement/Recurrent Gradient Boosting/Adversarial); Agents/Case Based Reasoning/Sentiment Analysis; Biomedical - Image Analysis; CNN - Convolutional Neural Networks YOLO CNN; Cyber Security/Anomaly Detection; Explainable AI/Social Impact of AI; Graph Neural Networks/Constraint Programming; IoT/Fuzzy Modeling/Augmented Reality; LEARNING (Active-AutoEncoders-Federated); Machine Learning; Natural Language; Optimization-Genetic Programming; Robotics; Spiking NN; and Text Mining /Transfer Learning.

Includes author index.

References -- CaTabRa: Efficient Analysis and Predictive Modeling of Tabular Data -- 1 Introduction -- 1.1 Related Work -- 2 The CaTabRa System -- 2.1 General Workflow -- 2.2 Model Training and Hyperparameter Tuning: AutoML -- 2.3 Performance Reports -- 2.4 Model Explanations -- 2.5 Out-of-Distribution Detection -- 2.6 Utilities for Working with Longitudinal Data -- 3 Use-Cases -- 3.1 Use-Case 1: COVID-19 Prediction -- 3.2 Use-Case 2: ICU Mortality Prediction -- 4 Conclusion -- References -- Explaining Machine Learning-Based Feature Selection of IDS for IoT and CPS Devices -- 1 Introduction

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Online resource; title from PDF title page (SpringerLink, viewed June 8, 2023).

IFIP advances in information and communication technology ; 676.

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