Author

N. Abu-Hamdeh

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Open access Oct 2026

Computational modeling and optimized machine learning for multidrug solubility prediction in supercritical carbon dioxide.

Sparse, leakage-controlled calibration is established as a more defensible use case than unconditional prediction for unseen drugs, while identifying the need for richer molecular descriptors and external prospective validation.

N. Abu-Hamdeh, H. Hijji, K. Almitani · Frontiers in Medicine · 0 citations

Open access Oct 2026

AI-driven predictive modeling of supercritical CO2 solubility using cheetah-optimized machine learning

Artificial intelligence (AI) and machine-learning models provide powerful data-driven tools for modeling complex nonlinear systems and predicting process behavior from limited experimental data. In this study, an AI-driven predictive framework is developed for modeling paracetamol solubility and solvent density in su...

Almuhannad S. Alorfi, N. Abu-Hamdeh · Frontiers in Medicine · 0 citations

Open access Sep 2026

Thermal analysis and CFD-assisted machine learning prediction of temperature fields in direct-contact membrane distillation using darts game optimization

In this study, three regression techniques—multi-layer perceptron (MLP), Gaussian process regression (GPR), and weighted least squares (WLS)—were evaluated to predict the spatial temperature distribution, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb}...

Ahmed B. Khoshaim, N. Abu-Hamdeh, A. Aljinaidi · Scientific Reports · 0 citations

Open access Sep 2026

Data-driven modeling of supercritical CO₂ processing with optimized machine learning: prediction of clobetasol propionate solubility

Machine learning can be used to support data-driven modeling of supercritical CO₂ processing. The method of machine learning modeling is applied in this work for evaluation of small-molecule processing under supercritical conditions. As a necessary step, the solute solubility in the solvent is evaluated via different m...

N. Abu-Hamdeh, M. Ajour · Frontiers in Medicine · 0 citations

Open access Sep 2026

Leakage-aware machine learning for data-driven performance prediction of metal–organic framework systems

Metal–organic frameworks (MOFs) are highly tunable porous materials whose performance is governed by complex interactions among structural, chemical, material, and operating variables. This study develops a leakage-aware, data-driven framework for predicting two distinct MOF performance endpoints: loading capacity an...

N. Abu-Hamdeh, M. Ajour, A. Milyani et al. · Frontiers in Medicine · 0 citations

Open access Aug 2026

Utilization of machine learning models and grey wolf optimization method in estimation of pharmaceutical solubility in supercritical CO2

Accurate prediction of solubility and solvent density in supercritical fluids is essential for the efficient design and optimization of pharmaceutical and chemical processes. In this study, three machine learning regression models—Elastic Net Regression (ENR), Orthogonal Matching Pursuit (OMP), and Gaussian Process Reg...

N. Abu-Hamdeh, A. Aljinaidi, Ahmed B. Khoshaim · Frontiers in Chemistry · 0 citations

Open access Sep 2026

Advanced data driven models based on machine learning for detection of faults and failures in solar based renewable energy systems

A novel framework that integrates Extended Kalman Filter (EKF) state estimation with uncertainty-aware graph learning for photovoltaic (PV) array fault detection and localization and demonstrates strong robustness to sensor noise and transient faults by leveraging physical uncertainty to guide graph topology.

Saud Wasly, N. Abu-Hamdeh · Scientific Reports · 0 citations