In multi-party deep learning, multiple participants jointly train a deep learning model through
a central server to achieve common objectives without sharing their private data. Recently, a …

Living lab, one of the recent emerging smart city concepts, faces long-term sustainability
challenges associated with its complexity and breadth of use. To be efficient, it must rely on …

With the rapid development of society and economy, people's living standards are improving
day by day, and increasingly attention is paid to physical health, which has set off a fitness …

In this paper we study the performance of two original adaptive unsupervised novelty
detection methods (NDMs) on data with concept drift. Newly, the concept drift is considered …

In order to reduce the torque ripple for switched reluctance motor (SRM), the learning error
preprocessing‐based torque–flux linkage recurrent neural network adaptive inversion …

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS)
stability for weight convergence of a broad family of in-parameter-linear nonlinear neural …

We present AISLEX, an online anomaly detection module based on the Learning Entropy
algorithm, a novel machine learning-based information measure that quantifies the learning …

为有效处理物联网大规模应用所带来的海量数据, 深度学习在物联网环境中得到广泛应用. 然而,
深度模型在训练过程中, 存在推理攻击, 模型逆向攻击等安全威胁, 这会导致输入模型中的原始 …

A study of machine learning approaches for temperature field (rake) prediction in a
turboprop engine is presented. The potential of supervised machine learning and shallow …

This article investigates an unconventional approach to solving the control of lateral
displacement for railway bogie wheelsets using recurrent higher order neural units …