自适应cssa:基于MapReduce框架的自适应鸡松鼠搜索算法驱动的深度信念网络，用于学生压力水平和退学预测

IF 2.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Social Network Analysis and Mining Pub Date : 2023-05-23 DOI:10.1007/s13278-023-01090-z

V. Kamakshamma, K. Bharati

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Adaptive-CSSA: adaptive-chicken squirrel search algorithm driven deep belief network for student stress-level and drop out prediction with MapReduce framework

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来源期刊

Social Network Analysis and Mining COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.70

自引率

14.30%

发文量

141

期刊介绍： Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. We solicit experimental and theoretical work on social network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science and computer science. The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (e.g. search) and social activities (e.g. discovery of potential friends), the analysis of user behavior in open forums (e.g. conventional sites, blogs and forums) and in commercial platforms (e.g. e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable and customizable social network infrastructure, identification and discovery of complex, dynamics, growth, and evolution patterns using machine learning and data mining approaches or multi-agent based simulation; (3) social network analysis and mining for open source intelligence and homeland security. Papers should elaborate on data mining and machine learning or related methods, issues associated to data preparation and pattern interpretation, both for conventional data (usage logs, query logs, document collections) and for multimedia data (pictures and their annotations, multi-channel usage data). Topics include but are not limited to: Applications of social network in business engineering, scientific and medical domains, homeland security, terrorism and criminology, fraud detection, public sector, politics, and case studies.