A review of dimensionality reduction techniques for efficient computation
S Velliangiri, S Alagumuthukrishnan - Procedia Computer Science, 2019 - Elsevier
Dimensionality Reduction (DR) is the pre-processing step to remove redundant features,
noisy and irrelevant data, in order to improve learning feature accuracy and reduce the …
noisy and irrelevant data, in order to improve learning feature accuracy and reduce the …
Review of feature selection approaches based on grou** of features
With the rapid development in technology, large amounts of high-dimensional data have
been generated. This high dimensionality including redundancy and irrelevancy poses a …
been generated. This high dimensionality including redundancy and irrelevancy poses a …
Fake news detection using a blend of neural networks: An application of deep learning
Fake news and its consequences carry the potential of impacting different aspects of
different entities, ranging from a citizen's lifestyle to a country's global relations, there are …
different entities, ranging from a citizen's lifestyle to a country's global relations, there are …
Knowledge Extraction from PV Power Generation with Deep Learning Autoencoder and Clustering-Based Algorithms
The unpredictable nature of photovoltaic solar power generation, caused by changing
weather conditions, creates challenges for grid operators as they work to balance supply …
weather conditions, creates challenges for grid operators as they work to balance supply …
Image segmentation using deep learning techniques in medical images
Nowadays, medical field is a one with a need for paramount concern and research where
medical sciences are at a stage that needs extensive research and technical proposals so …
medical sciences are at a stage that needs extensive research and technical proposals so …
A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects
Clustering is a set of essential mathematical techniques in artificial intelligence and machine
learning for analyzing massive amounts of data generated by applications. Clustering uses …
learning for analyzing massive amounts of data generated by applications. Clustering uses …
Algorithmic analysis for dental caries detection using an adaptive neural network architecture
Objectives AI techniques have lifelong impact in biomedics and widely accepted outcomes.
The sole objective of the study is to evaluate accurate detection of caries using feature …
The sole objective of the study is to evaluate accurate detection of caries using feature …
An autoencoder-based arithmetic optimization clustering algorithm to enhance principal component analysis to study the relations between industrial market stock …
CH Yang, B Lee, YI Lee, YF Chung, YD Lin - Expert Systems with …, 2025 - Elsevier
Traditional methods of forecasting and analyzing property trends using statistical analysis
and questionnaires are limited; in particular, they are too slow to provide insights based on …
and questionnaires are limited; in particular, they are too slow to provide insights based on …
ISBFK-means: A new clustering algorithm based on influence space
Y Yang, J Cai, H Yang, Y Li, X Zhao - Expert Systems with Applications, 2022 - Elsevier
The time overhead is huge and the clustering quality is unstable when running the K-means
algorithm on massive raw data. To solve these problems, the concept of the influence space …
algorithm on massive raw data. To solve these problems, the concept of the influence space …
Unsupervised machine learning for lithological map** using geochemical data in covered areas of **ing, China
Application of (supervised and unsupervised) machine learning algorithms to big
geoscience data can facilitate intelligent lithological map** and interpretation in a data …
geoscience data can facilitate intelligent lithological map** and interpretation in a data …