Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing
Given the recent advances in remote sensing analytics, cloud computing, and machine
learning, it is imperative to evaluate capabilities of remote sensing for water quality …
learning, it is imperative to evaluate capabilities of remote sensing for water quality …
Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing
KT Peterson, V Sagan, JJ Sloan - GIScience & Remote Sensing, 2020 - Taylor & Francis
Monitoring of inland water quality is of significant importance due to the increase in water
quality related issues, especially within the Midwestern United States. Traditional monitoring …
quality related issues, especially within the Midwestern United States. Traditional monitoring …
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
Ensemble of evolving data clouds and fuzzy models for weather time series prediction
This paper describes a variation of data cloud-based intelligent method known as typicality-
and-eccentricity-based method for data analysis (TEDA). The objective is to develop data …
and-eccentricity-based method for data analysis (TEDA). The objective is to develop data …
Empirical data analytics
In this paper, we propose an approach to data analysis, which is based entirely on the
empirical observations of discrete data samples and the relative proximity of these points in …
empirical observations of discrete data samples and the relative proximity of these points in …
A generalized methodology for data analysis
Based on a critical analysis of data analytics and its foundations, we propose a functional
approach to estimate data ensemble properties, which is based entirely on the empirical …
approach to estimate data ensemble properties, which is based entirely on the empirical …
[HTML][HTML] An evolving neuro-fuzzy system based on uni-nullneurons with advanced interpretability capabilities
PV de Campos Souza, E Lughofer - Neurocomputing, 2021 - Elsevier
This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-
uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for …
uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for …
A method for autonomous data partitioning
In this paper, we propose a fully autonomous, local-modes-based data partitioning
algorithm, which is able to automatically recognize local maxima of the data density from …
algorithm, which is able to automatically recognize local maxima of the data density from …
Autonomous anomaly detection for streaming data
Anomaly detection from data streams is a hotly studied topic in the machine learning
domain. It is widely considered a challenging task because the underlying patterns exhibited …
domain. It is widely considered a challenging task because the underlying patterns exhibited …
Unsupervised fault detection and prediction of remaining useful life for online prognostic health management of mechanical systems
Predictive maintenance allows industries to keep their production systems available as
much as possible. Reducing unforeseen shutdowns to a level that is close to zero has …
much as possible. Reducing unforeseen shutdowns to a level that is close to zero has …