Parallel and High-Performance Computing in Artificial Intelligence (eBook, PDF)

Redaktion: Raghuwanshi, Mukesh; Raut, Roshani; Jhaveri, Rutvij H.; Borkar, Pradnya

• Format: PDF

Produktbeschreibung

The book explores high-performance architectures for data-intensive applications, as well as efficient analytical strategies, to speed up data processing in applications in automation, machine learning, deep learning, bioinformatics, natural language processing, and vision intelligence.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 340
• Erscheinungstermin: 20. Mai 2025
• Englisch
• ISBN-13: 9781040332702
• Artikelnr.: 73814093

Autorenporträt

Dr. M. M. Raghuwanshi is the Dean of Engineering at S.B.Jain Institute of Technology Management and Research, Nagpur, India. Dr. Pradnya Borkar is an Associate Professor at the Department of Computer Science and Engineering and R&D Cell Incharge, Jhulelal Institute of Technology, Nagpur. Dr. Rutvij H. Jhaveri is an experienced researcher working in the Department of Computer Science & Engineering, Pandit Deendayal Energy University (PDEU/PDPU), Gandhinagar, India since Dec. 2019. Dr. Roshani Raut is an as Associate Professor in the Department of Information Technology and Associate Dean International Relations, in Pimpri Chinchwad College of Engineering, Pune, India.

Inhaltsangabe

1. Introduction to High Performance Computing Architectures 2. High
Performance Computing: Use Cases, API's and Applications 3. Parallelization
Techniques 4. High Performance Computing for Machine Learning 5.
Implementation of Parallel Computing with Artificial Intelligence in Big
Data Analytics 6. D-UNet: Deep Learning Architecture for Colon Polyp
Segmentation in Endoscopic Images 7. Early-Stage Plant Disease Detection
using YOLOv8 8. Landslide Detection Using Custom Deep Convolutional Neural
Network 9. GPU in Big Data: An Acceleration Technique 10. Use of NLP
Techniques and High-Performance Computing for Automated Knowledge-based
Ontology Construction of Saffron Crop 11. Implementing High-performance
Computing with Artificial Intelligence in Healthcare Systems 12. BLMP2CE:
Design of a Dual-Bioinspired Low-Complexity Data Mining Engine with
Parallel Processing for Automatic Cluster Analysis via Ensemble Learning
Operations 13. Deep Learning and Edge Computing with HPC 14. Usage of IoT,
High Performance Computing, Machine and Deep Learning in a Human Activity
Recognition (HAR) System: Challenges and Opportunities 15. Artificial
Intelligence in Industry: An Approach to Automation 16. Usage of IoT,
Artificial Intelligence and Machine Learning with HPC: Issues, Challenges,
and a Case Study 17. Advancing High-Performance Computing for AI in the Era
of Large-Scale Models: A Research Roadmap