A bayesian-neural-networks framework for scaling posterior distributions over different-curation datasets

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2023-12-26 DOI:10.1007/s10844-023-00837-6
Alfredo Cuzzocrea, Alessandro Baldo, Edoardo Fadda
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Abstract

In this paper, we propose and experimentally assess an innovative framework for scaling posterior distributions over different-curation datasets, based on Bayesian-Neural-Networks (BNN). Another innovation of our proposed study consists in enhancing the accuracy of the Bayesian classifier via intelligent sampling algorithms. The proposed methodology is relevant in emerging applicative settings, such as provenance detection and analysis and cybercrime. Our contributions are complemented by a comprehensive experimental evaluation and analysis over both static and dynamic image datasets. Derived results confirm the successful application of our proposed methodology to emerging big data analytics settings.

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在不同配置数据集上扩展后验分布的贝叶斯神经网络框架
在本文中,我们提出了一个基于贝叶斯神经网络(BNN)的创新框架,并对其进行了实验性评估,该框架用于在不同配置数据集上缩放后验分布。我们提出的另一项创新是通过智能采样算法提高贝叶斯分类器的准确性。所提出的方法适用于新兴的应用环境,如出处检测和分析以及网络犯罪。我们对静态和动态图像数据集进行了全面的实验评估和分析,对我们的贡献进行了补充。得出的结果证实,我们提出的方法可成功应用于新兴的大数据分析环境。
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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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