【IEEE独立出版】第八届大数据与应用统计国际学术研讨会（ISBDAS 2025）

ISBDAS 2024

会后5个月见刊，见刊后2个月检索

ISBDAS 2023

2023.03.10-12  上海

会后4个月见刊，见刊后1个月检索

ISBDAS 2022

2022.04.22-24  西宁

会后2个月见刊，见刊后1个月检索

ISBDAS 2021

2021.05.21-23  大理

会后1个月见刊，见刊后1个月检索

ISBDAS 2020

2020.07.10-12  昆明

会后1个月见刊，见刊后2个月检索

ISBDAS 2019

2019.09.20-22  大连

会后3个月见刊，见刊后1个月检索

ISBDAS 2018

2018.11.02-04  广州

会后2个月见刊，见刊后2个月检索

李哓黎教授

南洋理工大学

Prof. Philippe Fournier-Viger

深圳大学

黄翰教授

华南理工大学

Prof. LING Tok Wang

新加坡国立大学

Prof. Mohand Tahar Kechadi, 都柏林大学学院

章志華教授

关西国际大学

Prof. Malik Zawwar Hussain

旁遮普大学

杨燕教授

西南交通大学

Prof. Lazim Abdullah

马来西亚丁加奴大学

张天乐研究员

广东人工智能高等研究院

Prof. Fairouz Kamareddine

英国赫瑞瓦特大学

汤庸 教授，华南师范大学

Title: Cooperative Intelligent Applications based on SCHOLAT big Data

Abstract: Social networks have transformed the way people live and work. However, social networks are a double-edged sword. On the one hand, they facilitate communication among people; on the other hand, irrelevant information can disrupt life and work. Against the backdrop of the social collaboration needs in scientific research and teaching, we have created a social network for scholars (SCHOLAT), providing scholars with an independent academic space and a trustworthy collaboration platform. Currently, it has over 200,000 registered users (including more than 5,000 domestic and foreign institutions, covering all 985 universities), more than 10,000 research and teaching groups, as well as hundreds of millions of academic entities and their relationships. It has become a unique producer of big data in scientific research and teaching, and can provide a data and knowledge foundation for academic research and application development. This report mainly includes: 1) Research background and scientific issues; 2) SCHOLAT core and services; 3) Research on SCHOLAT big data, knowledge graph, and scholar large model; 4) SCHOLAT data foundation and SCHOLAT+ application ecosystem. Finally, several recent application achievements will be shared.

彭国超 教授，中山大学

Title: Realizing the power of AI: the Importance of Data, Human Needs and Scenes

Abstract: Artificial Intelligence (AI) has emerged as a revolutionary force, transforming industries and redefining the way we live and work. From intelligent virtual assistants to advanced autonomous vehicles, AI's potential seems limitless. However, to truly harness the power of AI and create meaningful and impactful applications, three key elements play a crucial role: data, human needs, and scenes. Understanding and integrating these components is essential for the successful development and deployment of AI technologies.

朱全新 教授，湖南师范大学

Title: Event-triggered control problems of stochastic nonlinear delay systems

Abstract: In this talk, we introduce the the event-triggered feedback control problem of stochastic nonlinear delay systems with exogenous disturbances. By introducing the notation of input-to-state practical stability and an event-triggered strategy, we establish the input-to-state practically exponential mean-square stability of the suggested system. Moreover, we investigate the stabilization result by designing the feedback gain matrix and the event-triggered feedback controller, which is expressed in terms of linear matrix inequalities. Also, the lower bounds of inter-execution times by the proposed event-triggered control method are obtained. Finally, an example is given to show the effectiveness of the proposed method. Compared with large number of results for discrete-time stochastic systems, only a few results have appeared on the event-triggered control for continuous-time stochastic systems. In particular, there has been no published papers on the event-triggered control for continuous-time stochastic delay systems. Our work is a first try to fill the gap on the topic.

戴宏亮 教授，广州大学

Title: Adaptive multiple kernel clustering using low-rank representation

Abstract: Multiple kernel clustering (MKC) effectively extracts intrinsic and complementary information from data by integrating diverse kernel functions. The allocation of kernel weights is crucial for MKC performance and is closely related to the relationships among kernel matrices. However, it is very difficult to fully capture the intricate relationships among high-dimensional matrices because previous research mostly relies on predefined metrics to characterize the correlation among kernel matrices. To address this challenge, a novel MKC model called AMKC-LRR is proposed that adaptively learns the interrelations among kernel matrices using low-rank representation and unifies this learning process with the clustering task within an optimization framework. Furthermore, an effective alternate optimization algorithm is designed to solve the resulting problem. Extensive experiments and statistical tests conducted on twelve commonly used benchmark datasets show that our proposed model performs favorably in comparison to state-of-the-art MKC methods. 

智能计算应用

模型与计算

智能计算算法

进化计算

数据挖掘

三元决策与机器学习

组合算法

数据和文本挖掘

知识推理

深度学习

博弈论

认知建模与计算

概率论与统计学

微分方程及其应用

离散数学与控制

线性代数及其应用

数值分析

运筹学与优化

近似理论

组合数学

可计算性理论

离散几何

矩阵计算

Mathematics是一本同行评审的开放获取期刊，为与数学相关的研究提供了一个高级论坛，由MDPI半月刊在线出版。

高可见度：在 Scopus、SCIE （Web of Science）、RePEc 和其他数据库中创建索引。

期刊排名： JCR - Q1 （Mathematics） / CiteScore - Q1 （General Mathematics ）

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