Area of research
Renewable Energy, Sustainability and the Environment · Materials Chemistry
Research interest
Research interests include Computer science, Artificial neural network, Artificial intelligence, Encoding (memory), Deep learning, and Preference.
From phenomics to post-phenomics: Multidisciplinary integration driving autonomous agricultural systems
Oxygen vacancy-rich amorphous FeNi hydroxide nanoclusters as an efficient electrocatalyst for water oxidation
An accurate and interpretable deep learning model for environmental properties prediction using hybrid molecular representations
A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints
An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures
Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm