Screen LibraryIntelligent computing : proceedings of the 2022 Computing Conference. Volume 2 /
The book, "Intelligent Computing - Proceedings of the 2022 Computing Conference", is a comprehensive collection of chapters focusing on the core areas of computing and their further applications in the real world. Each chapter is a paper presented at the Computing Conference 2022 held on J...
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| Corporate Authors: | |
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| Group Author: | |
| Published: |
Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | [2022] |
| Literature type: | Book |
| Language: | English |
| Series: |
Lecture notes in networks and systems,
volume 507 |
| Subjects: | |
| Summary: |
The book, "Intelligent Computing - Proceedings of the 2022 Computing Conference", is a comprehensive collection of chapters focusing on the core areas of computing and their further applications in the real world. Each chapter is a paper presented at the Computing Conference 2022 held on July 1415, 2022. Computing 2022 attracted a total of 498 submissions which underwent a double-blind peer-review process. Of those 498 submissions, 179 submissions have been selected to be included in this book. The goal of this conference is to give a platform to researchers with fundamental contributions and to be a premier venue for academic and industry practitioners to share new ideas and development experiences. We hope that readers find this book interesting and valuable as it provides the state-of-the-art intelligent methods and techniques for solving real-world problems. We also expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject. |
| Item Description: | International conference proceedings. |
| Carrier Form: | xii, 927 pages : illustrations (chiefly color) ; 24 cm. |
| Bibliography: | Includes bibliographical references and author index. |
| ISBN: |
9783031104633 3031104633 |
| Index Number: | QA75 |
| CLC: | TP3-532 |
| Call Number: | TP3-532/C738-10/2022/v.2 |
| Contents: | An Adaptive Geometry and Dual Graph Approach to Sign Prediction for Weighted and Signed Networks -- A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks -- Enhanced Deep Learning Framework for Fine-Grained Segmentation of Fashion and Apparel -- Linear Block and Convolutional MDS Codes to Required Rate, Distance and Type -- Run-Time Dependency Graph Models for Independently Developed Robotic Software Components -- A Raspberry Pi Computer Vision System for Self-Driving Cars. |