Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time. The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry.
信息科学部将发表原创性、创新性和创造性的研究成果。该杂志旨在为研究人员、开发人员、管理人员、战略规划人员、研究生和其他对信息、知识工程和智能系统领域的最新研究活动感兴趣的人提供服务。读者被假定对信息科学有共同的兴趣,但在工程、数学、统计学、物理学、计算机科学、细胞生物学、分子生物学、管理科学、认知科学、神经生物学、行为科学和生物化学等领域有不同的背景。
Cognitive Information Measurements: A New Perspective
来源期刊:Information sciencesDOI:10.1016/J.INS.2019.07.046
Combining unsupervised and supervised learning in credit card fraud detection
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.05.042
Targeting customers for profit: An ensemble learning framework to support marketing decision-making
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.05.027
HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.05.023
Self-attention convolutional neural network for improved MR image reconstruction
来源期刊:Information sciencesDOI:10.1016/J.INS.2019.03.080
Multi-view cluster analysis with incomplete data to understand treatment effects
来源期刊:Information sciencesDOI:10.1016/J.INS.2019.04.039
A novel approach for panel data: An ensemble of weighted functional margin SVM models
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.02.045
tcc2vec: RFM-informed representation learning on call graphs for churn prediction
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.02.044
An efficient linear programming based method for the influence maximization problem in social networks
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.07.043
Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics
来源期刊:Information SciencesDOI:10.1016/J.INS.2019.03.054