Results 61 to 70 of about 2,559 (181)

Optimization of Scene and Material Parameters for the Generation of Synthetic Training Datasets for Machine Learning-Based Object Segmentation

Computers
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to
Malte Nagel, Kolja Hedrich, Nils Melchert, Lennart Hinz, Eduard Reithmeier   +4 more
doaj   +1 more source

BayesianKAN: A reaction condition optimization framework integrating Kolmogorov‐Arnold network and Bayesian optimization

Smart Molecules, EarlyView.
A novel BayesianKAN framework integrates Kolmogorov‐Arnold networks with Bayesian optimization to efficiently navigate complex factor spaces, accelerating the discovery of optimal reaction conditions for chemical synthesis. Abstract Efficient optimization of chemical reaction conditions is crucial for enhancing reaction yield and selectivity, yet ...
Juntao Wang, Yujing Zhao, Peiyu Yi, Liyuan Duan, Qingwei Meng, Qilei Liu   +5 more
wiley   +1 more source

A Context‐Aware Decision Support Framework for Scientific Experiment Configuration

Software: Practice and Experience, EarlyView.
ABSTRACT Introduction Defining an experimental configuration is a complex decision problem for early‐stage researchers, who must map goals, constraints, and requirements onto datasets, algorithms, and parameter settings that directly affect experimental outcomes.
Pouriya Miri, Vlado Stankovski, Kristina Veljković, Petar Kochovski   +3 more
wiley   +1 more source

Analysis of Gradient Boosting Algorithms with Optuna Optimization and SHAP Interpretation for Phishing Website Detection

Journal of Applied Informatics and Computing
Phishing remains a persistent cybersecurity threat, evolving rapidly to bypass traditional blacklist-based detection systems. Machine Learning (ML) approaches offer a promising solution, yet finding the optimal balance between detection accuracy and ...
Rahmat Fauzi Abu Bakar, Majid Rahardi
doaj   +1 more source

Decreasing the Environmental Impact of the Electric Steelmaking Route Through Advanced Modelling Techniques

steel research international, EarlyView.
Ensemble models are adopted to estimate the sterile content of scraps arriving to the scrap yard. Feed‐forward neural networks are exploited to estimate steel composition and temperature after Ladle furnace. The models are validated on data from two steelworks very satisfactory results and are inherently transferable to other steelworks, as they are ...
Valentina Colla, Antonella Zaccara, Stefano Dettori, Laura Laid, Ismael Matino, Silvia Cateni, Teresa Annunziata Branca, Lorenzo Vannini   +7 more
wiley   +1 more source

Optuna-Optimized Machine Learning for Traffic Accident Severity Prediction in Jordan under Class Imbalance Conditions

Communications
A machine learning framework for predicting the traffic accident severity under class imbalance conditions is predented in Amman, Jordan. The methodology began with a preprocessing pipeline consisting of IQR-based (Interquartile range) outlier removal ...
Maen Qaseem Ghadi
doaj   +1 more source

Fine‐tuning ab initio XANES spectra calculations using the Bayesian optimization algorithm

Journal of Synchrotron Radiation, EarlyView.
A Bayesian optimization technique is used to tune the FEFF and FDMNES packages and to improve matching between theoretical and experimental spectra. The tests were performed on monometallic Ni, Fe and Pd K‐edge XANES spectra using several different spectrum similarity metrics.Theoretical modeling of X‐ray absorption near‐edge structure (XANES) spectra ...
Andrey A. Sapronov, Dmitry E. Doronkin, Jan-Dierk Grunwaldt   +2 more
wiley   +1 more source

Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce

TIERS Information Technology Journal
Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder ...
Zuriati Zuriati, Dewi Kania Widyawati, Oki Arifin, Kurniawan Saputra, Sriyanto Sriyanto, Asmala Ahmad   +5 more
doaj   +1 more source

Development and Clinical Utility of Machine Learning Models for Prediction of Same‐Day Discharge in Outpatient Hip and Knee Replacement: A Prognostic Study

Acta Anaesthesiologica Scandinavica, Volume 70, Issue 8, September 2026.
ABSTRACT Background Hip and knee replacement are common procedures with an increasing focus on same‐day surgery. However, capacity constraints limit the number of eligible patients actually being scheduled for same‐day discharge, calling for further selection of those with the highest likelihood of same‐day discharge.
Christoffer C. Jørgensen, Jakob B. Frederiksen, Henrik Kehlet, Martin Lindberg‐Larsen, Kirill Gromov, Claus Varnum, Thomas Jakobsen, Manuel Josef Bieder, Mikkel Rathsach Andersen, Søren Overgaard, Torben B. Hansen, Troels Petersen   +11 more
wiley   +1 more source

MSPCIFormer: A Multi‐Scale Patching Channel‐Independent Transformer for Cryptocurrency Price Forecasting

Expert Systems, Volume 43, Issue 9, September 2026.
ABSTRACT Forecasting cryptocurrency prices remains challenging due to extreme volatility, regime‐dependent dynamics, and unstable cross‐asset correlations. Statistical methods such as ARIMA and GARCH assume stationarity and linear dependence structures, making them inadequate for capturing non‐linear temporal patterns in high volatile cryptocurrency ...
Huali Zhao, Martin Crane, Marija Bezbradica   +2 more
wiley   +1 more source