Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
H Tao, SI Abba, AM Al-Areeq, F Tangang… - … Applications of Artificial …, 2024 - Elsevier
River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …
sustainability of water resources. The literature has shown great potential for nature-inspired …
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches
Data analysis remains a major challenge in the global application of comprehensive two-
dimensional liquid chromatography (LC× LC). Advanced chemometric tools have been …
dimensional liquid chromatography (LC× LC). Advanced chemometric tools have been …
Tuning OER electrocatalysts toward LOM pathway through the lens of multi-descriptor feature selection by artificial intelligence-based approach
From the thermodynamic and kinetic viewpoints, the oxygen evolution reaction (OER) is
central to the production of hydrogen through electrocatalytic water splitting process. As a …
central to the production of hydrogen through electrocatalytic water splitting process. As a …
A novel multi-model data-driven ensemble technique for the prediction of retention factor in HPLC method development
Reliable simulation of retention factor (k) is crucial in high-performance liquid
chromatography (HPLC) method development. In this research, three different Artificial …
chromatography (HPLC) method development. In this research, three different Artificial …
Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study
JC Agbasi, JC Egbueri - Journal of sedimentary environments, 2023 - Springer
Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
Interpretation the influence of hydrometeorological variables on soil temperature prediction using the potential of deep learning model
S Elsayed, M Gupta… - Knowledge …, 2023 - … journals.publicknowledgeproject.org
The importance of soil temperature (ST) quantification can contribute to diverse ecological
modelling processes as well as for agricultural activities. Over the literature, it was evident …
modelling processes as well as for agricultural activities. Over the literature, it was evident …
Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration
SI Abba, RA Abdulkadir, SS Sammen… - Hydrological …, 2021 - Taylor & Francis
Accurate prediction of dissolved oxygen (DO) concentration is important for managing
healthy aquatic ecosystems. This study investigates the comparative potential of the …
healthy aquatic ecosystems. This study investigates the comparative potential of the …
[HTML][HTML] Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds
Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this
study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference …
study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference …
Artificial intelligence-based models for the qualitative and quantitative prediction of aphytochemical compound using HPLC method
Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant
species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia …
species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia …
Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to …
The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals
requires the development of a robust model that represents the crude-to-chemical …
requires the development of a robust model that represents the crude-to-chemical …