Machine learning for perovskite solar cells and component materials: key technologies and prospects
Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …
Dataperf: Benchmarks for data-centric ai development
Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
Interpretable machine learning methods for predictions in systems biology from omics data
Machine learning has become a powerful tool for systems biologists, from diagnosing
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …
cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a …
Data-centric artificial intelligence: A survey
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …
of its great success is the availability of abundant and high-quality data for building machine …
Gene selection with Game Shapley Harris hawks optimizer for cancer classification
S Afreen, AK Bhurjee, RM Aziz - Chemometrics and Intelligent Laboratory …, 2023 - Elsevier
Cancer disease has been classified as a perilous disease for humans, being the second
leading cause of death globally. Even advanced-stage diagnosis may not be effective in …
leading cause of death globally. Even advanced-stage diagnosis may not be effective in …
Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection
Feature selection and hyper-parameters optimization (tuning) are two of the most important
and challenging tasks in machine learning. To achieve satisfying performance, every …
and challenging tasks in machine learning. To achieve satisfying performance, every …
Robust biomarker screening from gene expression data by stable machine learning-recursive feature elimination methods
Recently, identifying robust biomarkers or signatures from gene expression profiling data
has attracted much attention in computational biomedicine. The successful discovery of …
has attracted much attention in computational biomedicine. The successful discovery of …
SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers
Survival models exist to study relationships between biomarkers and treatment effects. Deep
learning-powered survival models supersede the classical Cox proportional hazards …
learning-powered survival models supersede the classical Cox proportional hazards …
Feature selection in machine learning for perovskite materials design and discovery
J Wang, P Xu, X Ji, M Li, W Lu - Materials, 2023 - mdpi.com
Perovskite materials have been one of the most important research objects in materials
science due to their excellent photoelectric properties as well as correspondingly complex …
science due to their excellent photoelectric properties as well as correspondingly complex …
[HTML][HTML] FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data
High dimensional and small samples characterize gene expression data and contain a large
number of genes unrelated to disease. Feature selection improves the efficiency of disease …
number of genes unrelated to disease. Feature selection improves the efficiency of disease …