Scientific simulation can generate petabyte-level data per run nowadays. To significantly
reduce the data size while simultaneously maintaining the compression quality based on …

This chapter emphasizes on the role played by rough set theory (RST) within the broad field
of Machine Learning (ML). As a sound data analysis and knowledge discovery paradigm …

In synthetic aperture radar (SAR) target recognition, the amount of target data increases
continuously, and thus SAR automatic target recognition (ATR) systems are required to …

In practical situations, dynamic covering decision information systems that change over time
are of interest because databases of this kind are frequently encountered. Incremental …

We introduce a modeling framework for the investigation of on-line machine learning
processes in non-stationary environments. We exemplify the approach in terms of two …

Unstructured data are irregular information with no predefined data model. Streaming data
which constantly arrives over time is unstructured, and classifying these data is a tedious …

Collaborative Filtering (CF) is a well-known approach for Recommender Systems (RS). This
approach extrapolates rating predictions from ratings given by user on items, which are …

Abstract Background Artificial Intelligence (AI)-based solutions for Gleason grading hold
promise for pathologists, while image quality inconsistency, continuous data integration …

Cross-modal hashing (CMH) models are introduced to significantly reduce the cost of large-
scale cross-modal data retrieval systems. In many realworld applications, however, data of …

数字孪生是一种将物理实体数字化的技术, 通过建立虚拟的数字孪生模型模拟实际的物理过程,
以便进行模拟仿真, 数据分析和优化设计等操作. 鉴于此, 分析数字孪生技术在复杂工业生产中的 …