0020-0255

Information Sciences （ISCI） - Volumes 355–356 论文列表

点击这里查看 Information Sciences 的JCR分区、影响因子等信息
卷期号： Volumes 355–356
发布时间： 10 August 2016
卷期年份： 2016
卷期官网： https://www.sciencedirect.com/journal/information-sciences/vol/355/suppl/C
本期论文列表
Editorial Board

Continuous leakage-resilient certificate-based encryption

Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks – The novel coevolution schema combined with linkage learning

A weighted local view method based on observation over ground truth for community detection

QualityCover: Efficient binary relation coverage guided by induced knowledge quality

Sparse representation matching for person re-identification

A delay-resilient and quality-aware mechanism over incomplete contextual data streams

Finding top-k influential users in social networks under the structural diversity model

Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances

A probabilistic ranking framework for web-based relational data imputation

GP made faster with semantic surrogate modelling

Design of polynomial fuzzy observer–controller with membership functions using unmeasurable premise variables for nonlinear systems

NICGAR: A Niching Genetic Algorithm to mine a diverse set of interesting quantitative association rules

A multivariate statistical combination forecasting method for product quality evaluation

Parameter estimation algorithms for multivariable Hammerstein CARMA systems

A model based on subjective linguistic preference relations for group decision making problems

Investigating the effects of time-delays on stochastic stability and designing l1-gain controllers for positive discrete-time Markov jump linear systems with time-delay

Cost-sensitive rough set approach

On generalized versions of central limit theorems for IF-events

Circular inter–intra pixels bit-level permutation and chaos-based image encryption

An interval extension of homogeneous and pseudo-homogeneous t-norms and t-conorms

Using unsupervised information to improve semi-supervised tweet sentiment classification