Xu / Pan / Yu | Data Science | Buch | 978-981-97-8745-6 | sack.de

Buch, Englisch, Band 2214, 347 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 557 g

Reihe: Communications in Computer and Information Science

Xu / Pan / Yu

Data Science

10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, Macao, China, September 27-30, 2024, Proceedings, Part II
2024
ISBN: 978-981-97-8745-6
Verlag: Springer Nature Singapore

10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, Macao, China, September 27-30, 2024, Proceedings, Part II

Buch, Englisch, Band 2214, 347 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 557 g

Reihe: Communications in Computer and Information Science

ISBN: 978-981-97-8745-6
Verlag: Springer Nature Singapore


This three-volume set CCIS 2213-2215 constitutes the refereed proceedings of the 10th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024, held in Macau, China, during September 27–30, 2024.

The 74 full papers and 3 short papers presented in these three volumes were carefully reviewed and selected from 249 submissions.

The papers are organized in the following topical sections:

Part I: Novel methods or tools used in big data and its applications; applications of data science.

Part II: Education research, methods and materials for data science and engine; data security and privacy; big data mining and knowledge management.

Part III: Infrastructure for data science; social media and recommendation system; multimedia data management and analysis.

Xu / Pan / Yu Data Science jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


.- Education research, methods and materials for data science and engine.

.- An empirical study of the factors influencing the improvement of education
quality within higher education institutions·

.- Study on the Intercultural Competence of Students in Hainan Vocational College.

.- Study on the Communicative Competence of Students of Tourism-related Majors
in Hainan Vocational Colleges.

.- Research on the Learning Adaptability and Learning Effectiveness of College
Students under the Background of Digital Education.

.- Research on the Adaptability of Vocational College Majors and Industry Empirical
Study Based on 14 Vocational Colleges in Hainan,China.

.- Practice of the Campus Data Middle Platform Based on Lakehouse Integrated
Architecture.

.- Data Security and Privacy.

.- Reversible Data Hiding for 3D Mesh Model Based on Block Modulus Encryption
and Multi-MSB Prediction.

.- QR code digital watermarking algorithm based  on GWO.

.- Fast CKKS Algorithm in the SEAL Library.

.- A Transformer-based Video Colorization Method Fusing Local Self-Attention and
Bidirectional Optical Flow.

.- An NTRU Lattice-Based Chameleon Hash Scheme for Redactable Blockchain Applications.

.- Traceable Decentralized Policy-Based Chameleon Hash Scheme for Blockchain
Rewriting·

.- SECURE IDENTITY AUTHENTICATION PROTOCOL BASED ON BLOCKCHAIN
IN SMART HOME.

.- False Data Injection Attack Detection Method Based on Long Time Series
Prediction.

.- A Hybrid Iris Recognition System Model based on Presentation Attack Detection
and Traffic Monitoring Module on AIoT System.

.- Big Data Mining and Knowledge Management.

.- Leveraging Spatial Characteristics in Trajectory Compression: An Angle-based
Bounded-error Method.

.- HENF: Hierarchical Entity Neighbor Multi-Relational Fusion Network for
Knowledge Graph Completion.

.- TCB Intrusion Detection Method Based on Data Enhancement.

.- Multi-source Heterogeneous Data Joint Diagnosis Method for Transformers Based
on D-S Evidence Theory.

.- Progressive Federated Learning Scheme Based on Model Pruning.

.- Privacy Protection Data Aggregation Scheme Against Quantum Attacks.

.- LOCATION DATA QUADTREE PARTITIONINGALGORITHM BASED ON
DIFFERENTIAL PRIVACY.

.- RLART: An Adaptive Radix Tree Based on Deep  Reinforcement Learning.



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.