A review of principal component analysis algorithm for dimensionality reduction
BMS Hasan, AM Abdulazeez - Journal of Soft Computing …, 2021 - publisher.uthm.edu.my
Big databases are increasingly widespread and are therefore hard to understand, in
exploratory biomedicine science, big data in health research is highly exciting because data …
exploratory biomedicine science, big data in health research is highly exciting because data …
From DFT to machine learning: recent approaches to materials science–a review
Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …
and complexity of generated data. This massive amount of raw data needs to be stored and …
% V Bur index and steric maps: from predictive catalysis to machine learning
Steric indices are parameters used in chemistry to describe the spatial arrangement of
atoms or groups of atoms in molecules. They are important in determining the reactivity …
atoms or groups of atoms in molecules. They are important in determining the reactivity …
Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Artificial intelligence has seen several breakthroughs in recent years, with games often
serving as milestones. A common feature of these games is that players have perfect …
serving as milestones. A common feature of these games is that players have perfect …
Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things
The field of photovoltaics gives the opportunity to make our buildings ''smart''and our
portable devices “independent”, provided effective energy sources can be developed for use …
portable devices “independent”, provided effective energy sources can be developed for use …
State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery
Abstract Machine learning (ML) has emerged as a significant tool in the field of biorefinery,
offering the capability to analyze and predict complex processes with efficiency. This article …
offering the capability to analyze and predict complex processes with efficiency. This article …
Logistic regression model training based on the approximate homomorphic encryption
Background Security concerns have been raised since big data became a prominent tool in
data analysis. For instance, many machine learning algorithms aim to generate prediction …
data analysis. For instance, many machine learning algorithms aim to generate prediction …
Towards operando computational modeling in heterogeneous catalysis
L Grajciar, CJ Heard, AA Bondarenko… - Chemical Society …, 2018 - pubs.rsc.org
An increased synergy between experimental and theoretical investigations in
heterogeneous catalysis has become apparent during the last decade. Experimental work …
heterogeneous catalysis has become apparent during the last decade. Experimental work …
Algorithm supported induction for building theory: How can we use prediction models to theorize?
Across many fields of social science, machine learning (ML) algorithms are rapidly
advancing research as tools to support traditional hypothesis testing research (eg, through …
advancing research as tools to support traditional hypothesis testing research (eg, through …
“Memo” functions and machine learning
D Michie - Nature, 1968 - nature.com
ciency. This way the whole visible spectrum can be Page 1 NATURE. VOL. 218, APRIL 6, 1963
Photolysis. Various dyes can be pumped with a primary laser and they convert the original …
Photolysis. Various dyes can be pumped with a primary laser and they convert the original …