A roadmap for multi-omics data integration using deep learning
High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …
amount of multi-omics data for various applications. These data have revolutionized …
Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects
Photovoltaic (PV) systems are subject to failures during their operation due to the aging
effects and external/environmental conditions. These faults may affect the different system …
effects and external/environmental conditions. These faults may affect the different system …
From characterization to discovery: artificial intelligence, machine learning and high-throughput experiments for heterogeneous catalyst design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Unsupervised and semi‐supervised learning: The next frontier in machine learning for plant systems biology
J Yan, X Wang - The Plant Journal, 2022 - Wiley Online Library
Advances in high‐throughput omics technologies are leading plant biology research into the
era of big data. Machine learning (ML) performs an important role in plant systems biology …
era of big data. Machine learning (ML) performs an important role in plant systems biology …
Feature inference attack on shapley values
As a solution concept in cooperative game theory, Shapley value is highly recognized in
model interpretability studies and widely adopted by the leading Machine Learning as a …
model interpretability studies and widely adopted by the leading Machine Learning as a …
Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
Fault detection and diagnosis (FDD) in the photovoltaic (PV) array has become a challenge
due to the magnitudes of the faults, the presence of maximum power point trackers, non …
due to the magnitudes of the faults, the presence of maximum power point trackers, non …
An enhanced ensemble learning-based fault detection and diagnosis for grid-connected PV systems
The main objective of this article is to develop an enhanced ensemble learning (EL) based
intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high …
intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high …
Distributed monitoring of nonlinear plant-wide processes based on GA-regularized kernel canonical correlation analysis
W **, W Wang, Y Wang, Z Cao, Q Jiang - Reliability Engineering & System …, 2024 - Elsevier
Fault detection and diagnosis is important for ensuring process safety and is gaining
increasing attention in the system safety field. A regularized kernel canonical correlation …
increasing attention in the system safety field. A regularized kernel canonical correlation …
Active Learning for Adsorption Simulations: Evaluation, Criteria Analysis, and Recommendations for Metal–Organic Frameworks
High-throughput molecular simulations and machine learning (ML) have been implemented
to adequately screen a large number of metal–organic frameworks (MOFs) for applications …
to adequately screen a large number of metal–organic frameworks (MOFs) for applications …
Fault identification of photovoltaic array based on machine learning classifiers
Fault identification in Photovoltaic (PV) array is a contemporary research topic motivated by
the higher penetration levels of PV systems in recent electrical grids. Therefore, this work …
the higher penetration levels of PV systems in recent electrical grids. Therefore, this work …