Titlebook: Big Data and Security; 5th International Co Yuan Tian,Tinghuai Ma,Muhammad Khurram Khan Conference proceedings 2024 The Editor(s) (if appli

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书目名称Big Data and Security影响因子(影响力)




书目名称Big Data and Security影响因子(影响力)学科排名




书目名称Big Data and Security网络公开度




书目名称Big Data and Security网络公开度学科排名




书目名称Big Data and Security被引频次




书目名称Big Data and Security被引频次学科排名




书目名称Big Data and Security年度引用




书目名称Big Data and Security年度引用学科排名




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书目名称Big Data and Security读者反馈学科排名

Lacerate

Big Data and Security978-981-97-4387-2Series ISSN 1865-0929 Series E-ISSN 1865-0937

行业

Conference proceedings 2024ina, during December 22–24, 2023. ..The 35 full papers and 1 short paper were carefully reviewed and selected from 161 submissions. They are organized in topical sections as follows: ..Part One: Big Data & New Method and Artificial Intelligence & Machine Learning..Part Two: Data Technology & Network Security and IoT Security & Privacy Protection..

脾气暴躁的人

https://doi.org/10.1007/978-981-97-4387-2Security and Privacy; Big Data; Block Chain; Distributed computing methodologies; Security Services; Intr

Fibrin

flavonoids

https://doi.org/10.1007/3-540-34011-4ecognition often focuses on facial features from single facial images, without taking into account personal aging patterns. We propose an algorithm for personal aging patterns based on manifold learning (MLPAP). MLPAP combines manifold learning and two deep learning architecture Convolutional Neural

exacerbate

Untersuchungsmethoden der Mikrostruktur,dependencies, limiting their generality and scalability in real engineering projects. To address this issue, this paper introduces a Three Layer Chinese Sentiment Polarity Detection Framework (.). This framework decouples the final polarity detection from language processing by dividing the task int

Lipoma

,Teilchengehärtete Legierungen,lectual output is a key representation of discipline construction. We created an algorithm to identify development features of disciplines, including temporal trend, research hotspots, and mutation characteristics. Using CNKI as data source, with the aid of scientific knowledge graph and social netw

MILL

藕床生厌倦