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