Learning under concept drift: A review
Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …
data overtime. Concept drift research involves the development of methodologies and …
Artificial intelligence-driven biomedical genomics
As genomic research becomes more complex and data-rich, artificial intelligence (AI) has
emerged as a crucial tool for processing and analyzing high-dimensional genomic data …
emerged as a crucial tool for processing and analyzing high-dimensional genomic data …
Artificial intelligence in recommender systems
Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …
previous behaviors and predicting their current preferences for particular products. Artificial …
Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification
Aspect-level sentiment classification is a fundamental subtask of fine-grained sentiment
analysis. The syntactic information and commonsense knowledge are important and useful …
analysis. The syntactic information and commonsense knowledge are important and useful …
Artificial Neural Network (ANN)-Bayesian Probability Framework (BPF) based method of dynamic force reconstruction under multi-source uncertainties
In view of the universal existence of multi-source uncertainty factors in engineering
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
A novel framework for detecting social bots with deep neural networks and active learning
Microblogging is a popular online social network (OSN), which facilitates users to obtain and
share news and information. Nevertheless, it is filled with a huge number of social bots that …
share news and information. Nevertheless, it is filled with a huge number of social bots that …
A cross-domain recommender system with kernel-induced knowledge transfer for overlapping entities
The aim of recommender systems is to automatically identify user preferences within
collected data, then use those preferences to make recommendations that help with …
collected data, then use those preferences to make recommendations that help with …
Robust anomaly detection for multivariate time series through temporal GCNs and attention-based VAE
Anomaly detection on multivariate time series (MTS) is of great importance in both data
mining research and industrial applications. While a handful of anomaly detection models …
mining research and industrial applications. While a handful of anomaly detection models …
Rough set based semi-supervised feature selection via ensemble selector
Similar to feature selection over completely labeled data, the aim of feature selection over
partially labeled data (semi-supervised feature selection) is also to find a feature subset …
partially labeled data (semi-supervised feature selection) is also to find a feature subset …
Multiple finite-time synchronization of delayed inertial neural networks via a unified control scheme
In this paper, a unified control framework is proposed to investigate the synchronization
problem of inertial neural networks (INNs). Via the proposed framework, the finite-time, fixed …
problem of inertial neural networks (INNs). Via the proposed framework, the finite-time, fixed …