Results 61 to 70 of about 4,087 (203)

Optuna-Optimized Ensemble and Neural Network Models for Static Characteristics Prediction of Active Bearings with Geometric Adjustments

Modelling
Active vibration control designs for journal bearings have improved rotordynamic stability and led to advancements in adjustable bearing types that enable precise control of bearing geometry. In this study, optimized machine learning (ML) algorithms were
Girish Hariharan, Ravindra Mallya, Nitesh Kumar, Deepak Doreswamy, Gowrishankar Mandya Chennegowda, Subraya Krishna Bhat   +5 more
doaj   +1 more source

Time Series Extrinsic Regression for Smart Agriculture: Field‐Level Crop Yield Prediction From Multisource Agricultural Time Series

Quality and Reliability Engineering International, EarlyView.
ABSTRACT The growing availability of temporal data in agriculture has created new opportunities for data‐driven decision support systems aimed at improving the efficiency, sustainability, and adaptability of agricultural systems. Remote sensing platforms, weather‐related datasets, and field‐management records provide information on crop development and
Amalia Vanacore, Armando Ciardiello, Gennaro Pio Auricchio, Luigi Uccello, Annalisa Izzo   +4 more
wiley   +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

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

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

Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models

IEEE Access
Artificial intelligence (AI) has been increasingly applied to solve complex real-world problems. One of the most significant challenges in AI lies in selecting and fine-tuning the optimal algorithm for a given task.
Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan   +3 more
doaj   +1 more source

Differentiation between bipolar versus unipolar depression by AI‐based multimodal behavioral analysis

Psychiatry and Clinical Neurosciences, EarlyView.
Background Given their differing pathophysiology and treatment approaches, the primary aim of the current study was to develop a methodology accurately distinguishing bipolar from unipolar depression. Methods Adults with bipolar disorder or major depressive disorder were recruited nationwide in Japan and assessed via recorded interviews.
Hidenori Yamasue, Sanskar Rahul Nanegaonkar, Yumi Kiyama, Mamindla Vishwa Raghava Reddy, Kotaro Ando, Shozo Kamiya, Jun Inoue, Asuka Sakurai, Tahichi Nakano, Amane Sato, Yosuke Kameno, Tomoyasu Wakuda, Keiichi Odagiri, Tadafumi Kato   +13 more
wiley   +1 more source

Optimizing XGBoost for Heart Disease Risk Classification Using Optuna and Random Search on the Behavioral Risk Factor Surveillance System (BRFSS) 2023 Dataset

Journal of Applied Informatics and Computing
Heart disease is a critical public health issue in Indonesia, contributing to approximately 1,5 million deaths annually. Although machine learning methods, particularly Extreme Gradient Boosting (XGBoost), have demonstrated strong performance in medical ...
Muhammad Dzaky, Adam Prayogo Kuncoro, Riyanto Riyanto   +2 more
doaj   +1 more source

Regression Model for a Sensor‐Integrating Jaw Coupling—Compensation for the Viscoelastic Material Behavior

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.
ABSTRACT In recent years, there has been an increasing demand to digitize production processes. In order to obtain the necessary data for these processes, numerous sensors must be installed on or in the machines. This can be difficult, especially when done retroactively, as the existing installation space must be able to accommodate the sensors.
Johannes D.M. Menning, Arthur Ewert, Artem Prokopchuk, Berthold Schlecht, E.‐F. Markus Vorrath, Thomas Wallmersperger   +5 more
wiley   +1 more source

Forecasting Topic, Word, and Hashtag Popularity on X (Twitter) Using LightGBM for Digital Marketing Optimization

Journal of Applied Informatics and Computing
This study presents a machine learning, based framework to forecast the popularity of topics, words, and hashtags on platform X (Twitter) for data-driven digital marketing optimization.
Deannisa Syafira Putri, Amri Muhaimin, Mohammad Idhom   +2 more
doaj   +1 more source