Intelligent systems and applications : proceedings of the 2023 Intelligent Systems Conference (IntelliSys). Volume 2 / Kohei Arai, editor.
The book is a unique collection of studies involving intelligent systems and applications of artificial intelligence in the real world to provide solutions to most vexing problems. IntelliSys received an overwhelming 605 papers which were put under strict double-blind peer-review for their novelty,...
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Full Text (via Springer) |
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| Format: | Conference Proceeding eBook |
| Language: | English |
| Published: |
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Springer,
2024.
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| Series: | Lecture notes in networks and systems ;
v. 823. |
| Subjects: |
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| 111 | 2 | |a Intelligent Systems Conference |d (2023 : |c Amsterdam, Netherlands ; Online) | |
| 245 | 1 | 0 | |a Intelligent systems and applications : |b proceedings of the 2023 Intelligent Systems Conference (IntelliSys). |n Volume 2 / |c Kohei Arai, editor. |
| 264 | 1 | |a Cham : |b Springer, |c 2024. | |
| 300 | |a 1 online resource (xii, 879 pages) : |b illustrations (some color). | ||
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| 490 | 1 | |a Lecture notes in networks and systems, |x 2367-3389 ; |v 823 | |
| 520 | |a The book is a unique collection of studies involving intelligent systems and applications of artificial intelligence in the real world to provide solutions to most vexing problems. IntelliSys received an overwhelming 605 papers which were put under strict double-blind peer-review for their novelty, originality and exhaustive research. Finally, 227 papers were sieved and chosen to be published in the proceedings. This book is a valuable collection of all the latest research in the field of artificial intelligence and smart systems. It provides a ready-made resource to all the readers keen on gaining information regarding the latest trends in intelligent systems. It also renders a sneak peek into the future world governed by artificial intelligence. | ||
| 500 | |a Includes author index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed April 25, 2024). | |
| 505 | 0 | |a Intro -- Preface -- Contents -- YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment -- 1 Introduction -- 2 The Fischertechnik Learning Factory -- 3 Dataset Preparation -- 4 YOLO Models -- 4.1 YOLOv1 -- 4.2 YOLOv3 -- 4.3 YOLOv5 -- 5 Detection Results -- 6 Summary and Conclusions -- References -- Towards End-to-End Escape in Urban Autonomous Driving Using Reinforcement Learning -- 1 Introduction -- 2 Materilas and Methods -- 2.1 Related Work -- 2.2 Experiments Setup -- 3 Results -- 3.1 Settings Evaluation -- 4 Conclusions -- References | |
| 505 | 8 | |a MS-ETL: An Architecture for the Multiple Data Source Extraction, Transformation, and Load Applied to Solar Flares Data -- 1 Introduction -- 2 Concepts and Background -- 3 Multiple Data Source Extraction, Transformation, and Load (MS-ETL) Architecture -- 3.1 The Extraction of the Series of Solar Satellite Images -- 3.2 Extraction of Solar Flares Data -- 4 Results and Discussions -- 5 Conclusion and Future Works -- References -- Wind Turbine Data-Driven Intelligent Fault Detection -- 1 Introduction -- 2 Intelligent Control Methodology -- 3 Proposed Strategy -- 4 Results and Discussion | |
| 505 | 8 | |a 5 Conclusion -- References -- Enhancing Decision Support Systems for the Energy Sector with Sustainable Artificial Intelligence Solutions -- 1 Introduction -- 2 Background -- 3 I-NERGY Framework -- 3.1 Overview -- 3.2 I-NERGY Use Cases -- 3.3 I-NERGY Assets and AIoD Experiments -- 4 Conclusion and Next Steps -- References -- Pre-trained Deep Learning Models for Chest X-Rays' Classification: Views and Age-Groups -- 1 Introduction -- 2 Pre-trained Models' Architecture -- 2.1 VGG16 -- 2.2 ResNet50 -- 2.3 Inception V3 -- 2.4 Xception -- 2.5 MobileNetV2 -- 2.6 DenseNet201 -- 2.7 EfficientNetB7 | |
| 505 | 8 | |a 3 Methodology -- 3.1 Data Preparation -- 3.2 Implementation -- 3.3 Evaluation -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Impact of Gender and Chest X-Ray View Imbalance in Pneumonia Classification Using Deep Learning -- 1 Introduction -- 2 Architecture and Methodology -- 2.1 Architecture -- 2.2 Datasets -- 3 Results -- 3.1 Training -- 3.2 Testing -- 3.3 Gender-Based Testing -- 3.4 View-Based Testing -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Gesture Recognition of Filipino Sign Language Using Convolutional and Long-Short Term Memory Neural Network | |
| 505 | 8 | |a 1 Introduction -- 2 Background and Related Works -- 2.1 Sign Language Recognition (SLR) Systems -- 2.2 Challenges on FSL Research -- 3 Methodology -- 3.1 Proposed Design System Architecture -- 3.2 Data Collection and Preparation -- 3.3 Training -- 4 Results and Discussion -- 5 Conclusion -- References -- Index Tracking Via Learning to Predict Market Sensitivities -- 1 Introduction -- 2 Related Work -- 2.1 Index Tracking via Machine Learning -- 2.2 Estimation of Market Sensitivities -- 3 Preliminaries -- 3.1 Notations -- 3.2 Single-Factor Model -- 3.3 Deep-Learning Models -- 4 Proposed Methods | |
| 650 | 0 | |a Artificial intelligence |v Congresses. |0 http://id.loc.gov/authorities/subjects/sh2007101476 | |
| 700 | 1 | |a Arai, Kohei, |e editor. |0 http://id.loc.gov/authorities/names/nb2015009350 | |
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| 830 | 0 | |a Lecture notes in networks and systems ; |v v. 823. |0 http://id.loc.gov/authorities/names/no2018045655 | |
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