The recommender system (RS) is a well-known practical application of the state-of-the-art
information filtering and machine learning technologies. Traditional recommendation …

In this research paper, we aim to investigate and address the limitations of recursive feature
elimination (RFE) and its variants in high-dimensional feature selection tasks. We identify …

This article is concerned with the secure state estimation problem for artificial neural
networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote …

Deep-learning-based fault diagnosis of wind turbine has played a significant role in
advancing the renewable energy industry. However, the imbalanced data sampled by the …

Monitoring and predicting crowd movements in indoor environments are of great importance
in crowd management to prevent crushing and trampling. Existing works mostly focused on …

Recommendation systems employ users history data records to predict their preference, and
have been widely used in diverse fields including biology, e-commerce, and healthcare …

High-fidelity sensor data gathered at high sampling rates is required by human context
recognition (HCR) to accurately predict the users' current context. This approach consumes …

With advances in machine learning and artificial intelligence, a considerable impact is
brought to all aspects of people's lifestyles in terms of work, social, and economy. Especially …

Sensor modality diversity as well as data annotation shortage are common challenges for
sensing-based human activity recognizing (HAR) in smartphone settings. In light of this …

A Complete Bibliography of ACM Transactions on Multimedia Computing, Communications
and Applications Page 1 A Complete Bibliography of ACM Transactions on Multimedia …