The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

L Sarmiento Varón, J González-Puelma… - Frontiers in public …, 2023 - frontiersin.org
The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in
modern times. The development of effective strategies to control the spread of SARS-CoV-2 …

New generation neurocomputing learning coupled with a hybrid neuro-fuzzy model for quantifying water quality index variable: A case study from Saudi Arabia

MS Manzar, M Benaafi, R Costache, O Alagha… - Ecological …, 2022 - Elsevier
Ensuring availability in terms of quality and quantity and sustainable management of safe,
affordable drinking water is one of the integral parts of envisioning the 2030 Sustainable …

A novel hybrid optimization approach for fault detection in photovoltaic arrays and inverters using AI and statistical learning techniques: a focus on sustainable …

A Abubakar, MM Jibril, CFM Almeida, M Gemignani… - Processes, 2023 - mdpi.com
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and
performance. Artificial intelligence (AI) learning can be used to quickly identify issues …

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

N Baig, SI Abba, J Usman, M Benaafi… - Environmental Science …, 2023 - pubs.rsc.org
The escalating quantity of wastewater from multiple sources has raised concerns about both
water reuse and environmental preservation. Therefore, there is a pressing need for …

Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical …

SI Abba, J Usman, I Abdulazeez, LT Yogarathinam… - RSC …, 2024 - pubs.rsc.org
Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of
lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery …

An overview of streamflow prediction using random forest algorithm

MM Jibril, A Bello, II Aminu, AS Ibrahim… - GSC Advanced …, 2022 - gsconlinepress.com
Since the first application of Artificial Intelligence in the field of hydrology, there has been a
great deal of interest in exploring aspects of future enhancements to hydrology. This is …

Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA, TARMA and ENNReg models

S Mati, M Radulescu, N Saqib, A Samour, GY Ismael… - Heliyon, 2023 - cell.com
This article investigates the performance of three models-Autoregressive Integrated Moving
Average (ARIMA), Threshold Autoregressive Moving Average (TARMA) and Evidential …

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

ZM Yaseen, WHMW Mohtar, RZ Homod, OA Alawi… - Chemosphere, 2024 - Elsevier
This study proposes different standalone models viz: Elman neural network (ENN), Boosted
Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As …

Neurocomputing modelling of hydrochemical and physical properties of groundwater coupled with spatial clustering, GIS, and statistical techniques

M Benaafi, MA Yassin, AG Usman, SI Abba - Sustainability, 2022 - mdpi.com
Groundwater (GW) is a critical freshwater resource for billions of individuals worldwide.
Rapid anthropogenic exploitation has increasingly deteriorated GW quality and quantity …

Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping

SI Abba, MA Yassin, MM Jibril, B Tawabini… - Process Safety and …, 2024 - Elsevier
Nitrate contamination in groundwater is a significant environmental concern that poses risks
to human health and ecosystems. Several goals and targets of Sustainable Development …