An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions
ZM Yaseen - Chemosphere, 2021 - Elsevier
The development of computer aid models for heavy metals (HMs) simulation has been
remarkably advanced over the past two decades. Several machine learning (ML) models …
remarkably advanced over the past two decades. Several machine learning (ML) models …
Human activity recognition data analysis: History, evolutions, and new trends
The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses
on generating innovative technology, products, and services to assist, medical care and …
on generating innovative technology, products, and services to assist, medical care and …
From center to surrounding: An interactive learning framework for hyperspectral image classification
Owing to rich spectral and spatial information, hyperspectral image (HSI) can be utilized for
finely classifying different land covers. With the emergence of deep learning techniques …
finely classifying different land covers. With the emergence of deep learning techniques …
Consolidated convolutional neural network for hyperspectral image classification
The performance of hyperspectral image (HSI) classification is highly dependent on spatial
and spectral information, and is heavily affected by factors such as data redundancy and …
and spectral information, and is heavily affected by factors such as data redundancy and …
An unsupervised chatter detection method based on AE and merging GMM and K-means
B Liu, C Liu, Y Zhou, D Wang, Y Dun - Mechanical Systems and Signal …, 2023 - Elsevier
During metal cutting, chatter is prone to the effects of poor surface quality and tool wear.
Therefore, chatter detection is becoming more and more important. The current hot methods …
Therefore, chatter detection is becoming more and more important. The current hot methods …
Spatial and spectral structure preserved self-representation for unsupervised hyperspectral band selection
As an effective manner to reduce data redundancy and processing inconvenience,
hyperspectral band selection aims to select a subset of informative and discriminative bands …
hyperspectral band selection aims to select a subset of informative and discriminative bands …
Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy
R Devendiran, AV Turukmane - Expert Systems with Applications, 2024 - Elsevier
Network intrusion is a huge harmful activity to the privacy of the data sharing network. The
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
activity will result in a cyber-attack, which causes damage to the system as well as the user's …
Can spectral information work while extracting spatial distribution?—An online spectral information compensation network for HSI classification
In the past few years, deep learning-based methods have shown commendable
performance for hyperspectral image (HSI) classification. Many works focus on designing …
performance for hyperspectral image (HSI) classification. Many works focus on designing …
Advanced plant disease segmentation in precision agriculture using optimal dimensionality reduction with fuzzy c-means clustering and deep learning
Analysis of hyperspectral imagery is a critical aspect of remote sensing in precision
agriculture, for which effective dimensionality reduction (DR) strategies for the inherent …
agriculture, for which effective dimensionality reduction (DR) strategies for the inherent …
Mutual information-driven feature reduction for hyperspectral image classification
A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral
wavelength bands, is a valuable source of data for ground cover examinations …
wavelength bands, is a valuable source of data for ground cover examinations …