Accelerating materials discovery using machine learning
Y Juan, Y Dai, Y Yang, J Zhang - Journal of Materials Science & …, 2021 - Elsevier
The discovery of new materials is one of the driving forces to promote the development of
modern society and technology innovation, the traditional materials research mainly …
modern society and technology innovation, the traditional materials research mainly …
Rebooting data-driven soft-sensors in process industries: A review of kernel methods
Soft-sensors usually assist in dealing with the unavailability of hardware sensors in process
industries, thus allowing for less fault occurrence and better control performance. However …
industries, thus allowing for less fault occurrence and better control performance. However …
MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism
One of the main problems with the joint use of multiple drugs is that it may cause adverse
drug interactions and side effects that damage the body. Therefore, it is important to predict …
drug interactions and side effects that damage the body. Therefore, it is important to predict …
[HTML][HTML] An artificial intelligence-based stacked ensemble approach for prediction of protein subcellular localization in confocal microscopy images
Predicting subcellular protein localization has become a popular topic due to its utility in
understanding disease mechanisms and developing innovative drugs. With the rapid …
understanding disease mechanisms and developing innovative drugs. With the rapid …
A survey of multi-label classification based on supervised and semi-supervised learning
M Han, H Wu, Z Chen, M Li, X Zhang - International Journal of Machine …, 2023 - Springer
Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …
[HTML][HTML] Research on multi-label user classification of social media based on ML-KNN algorithm
A Huang, R Xu, Y Chen, M Guo - Technological Forecasting and Social …, 2023 - Elsevier
Several research studies have been conducted on multi-label classification algorithms for
text and images, but few have been conducted on multi-label classification for users …
text and images, but few have been conducted on multi-label classification for users …
Contrastive graph learning long and short-term interests for POI recommendation
Modeling users' short-term dynamic and long-term static interests to enhance Point-of-
Interests (POI) recommendation performance has shown lots of advantages. Since users' …
Interests (POI) recommendation performance has shown lots of advantages. Since users' …
Identifying multi-functional bioactive peptide functions using multi-label deep learning
W Tang, R Dai, W Yan, W Zhang, Y Bin… - Briefings in …, 2022 - academic.oup.com
The bioactive peptide has wide functions, such as lowering blood glucose levels and
reducing inflammation. Meanwhile, computational methods such as machine learning are …
reducing inflammation. Meanwhile, computational methods such as machine learning are …
Compound fault diagnosis for photovoltaic arrays based on multi-label learning considering multiple faults coupling
For photovoltaic (PV) systems with complex operating environment and long operation time,
there are multiple faults coupled simultaneously. However, most of the existing fault …
there are multiple faults coupled simultaneously. However, most of the existing fault …
A generalized weighted distance k-nearest neighbor for multi-label problems
In multi-label classification, each instance is associated with a set of pre-specified labels.
One common approach is to use Binary Relevance (BR) paradigm to learn each label by a …
One common approach is to use Binary Relevance (BR) paradigm to learn each label by a …