Results 41 to 50 of about 1,984 (174)

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods

Urban Science
Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models.
Tris Kee, Winky K.O. Ho
doaj   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Using a hybrid attention mechanism as a method to improve the efficiency of network intrusion detection systems

Радіоелектронні і комп'ютерні системи
The subject matter of this article is a HybridAttention mechanism integrated into a deep neural architecture for Network Intrusion Detection Systems (NIDS). This study aims to develop and study a HybridAttention mechanism based on a combination of global
Andrii Nikitenko, Yevhen Bashkov
doaj   +3 more sources

Enhanced Modelling Performance with Boosting Ensemble Meta-Learning and Optuna Optimization

SN Computer Science
AbstractImproving modeling performance on imbalanced multi-class classification problems has continued to attract attention from researchers considering the critical and significant role such models should play in mitigating the prevalent problem. Ensemble Learning (EL) techniques are among the key methods utilized by researchers as they are known for ...
Tertsegha J. Anande, Sami Alsaadi, Mark S. Leeson   +2 more
openaire   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

Battery Energy, Volume 4, Issue 2, March 2025.
Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
wiley   +1 more source

Deep Learning Framework With Optuna-Based Hyperparameter Tuning for Predicting Dry Turning Process Performance of 42CrMo4 Steel

IEEE Access
This study presents an integrated experimental and data-driven modeling framework for predicting key machining responses such as tool wear (TW), material removal rate (MRR), and surface roughness (Ra) during dry turning of 42CrMo4 alloy steel ...
Vasanth Siva Kumar, Mogana Priya Chinnasamy, Swetha R Kumar   +2 more
doaj   +1 more source

Safety soft sensor development for pilot‐scale ilmenite electric arc furnace using long short‐term memory‐based architecture

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Ilmenite electric arc furnaces (EAFs) are used for smelting titanium‐iron oxide ore at high temperatures generated by electrical arcs to produce titanium slag and pig iron. As these units are pushed to their limits, ensuring safe and reliable operation becomes challenging.
Antony Gareau‐Lajoie, Djogap F. Chrysler J., Daniel Rodrigues, Marie‐Ève Gosselin, Moncef Chioua   +4 more
wiley   +1 more source

A Semi‐Automated Microfluidic Platform Employing Machine Learning Analysis to Study Adhesion Kinetics in Acute Myeloid Leukemia

Microscopy Research and Technique, EarlyView.
We have developed a semi‐automated shear flow platform using bright‐field optics and a machine‐learning analysis algorithm to dissect tumor‐microenvironment interactions. The algorithm quantifies the extent of adhesion at the single‐cell level and delivers consistent results within minutes instead of hours, facilitating high‐throughput analysis ...
Driti Ashok, Dennis Raith, Gabriel Kalweit, Maria Kalweit, Anusha Klett, Justus Duyster, Jonas Bermeitinger, Tanja Nicole Hartmann   +7 more
wiley   +1 more source

A Novel Identification Approach Using RFECV–Optuna–XGBoost for Assessing Surrounding Rock Grade of Tunnel Boring Machine Based on Tunneling Parameters

Applied Sciences
In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed.
Kebin Shi, Renyi Shi, Tao Fu, Zhipeng Lu, Jianming Zhang   +4 more
doaj   +1 more source