Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (eBook, PDF)

Redaktion: Lee, Roger

• Format: PDF

Produktbeschreibung

This book presents scientific results of the 22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021-Fall) which was held on November 24-26, 2021, at Taichung, Taiwan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them.

The conference organizers selected the best papers from those papers accepted for presentation at the conference. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 13 of most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 189
• Erscheinungstermin: 3. Januar 2022
• Englisch
• ISBN-13: 9783030923174
• Artikelnr.: 63248762

Inhaltsangabe

Segmentation of Spinal MRI Images and New Compression Fracture Detection.- Key issues for Digital Factory Designing and Planning: A Survey.- Concentration-based robot control method with FPGA.- SolarWinds Software Supply Chain Security: Better Protection with Enforced Policies and Technologies.- A Data Hiding Technology by Applying Interpolation in Extended Local Binary Pattern.- Insect Species Identification System Based on Deep Learning.- Study of DIFA based Learning Data Generating Methodology for Malware Detection.