Results 31 to 40 of about 1,984 (174)

Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction

Journal Medical Informatics Technology, 2023
Heart disease prediction is a critical area in healthcare, as early identification and accurate assessment of cardiovascular risks can lead to improved patient outcomes. This study explores the application of machine learning techniques for predicting heart disease.
openaire   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

Harnessing Large‐Scale Multi‐Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the General Population

Advanced Science, EarlyView.
Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang, Yang Liu, Mingyu Song, Ning Chen, Canqing Yu, Jun Lv, Eric Yuk Fai Wan, Lu Qi, Liming Li, Dianjianyi Sun, Bang Zheng   +10 more
wiley   +1 more source

Application of ADASYN and Optuna in the XGBoost Algorithm for Stunting Detection

Journal of Applied Informatics and Computing
This study aims to develop an early detection model for childhood stunting risk using a machine learning approach based on Extreme Gradient Boosting (XGBoost), integrated with the Adaptive Synthetic Sampling (ADASYN) technique for data balancing and ...
Fastabyq Putra Sadewa, Defri Kurniawan
doaj   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

An integrated machine learning and hyperparameter optimization framework for noninvasive creatinine estimation using photoplethysmography signals

Healthcare Analytics
Frequent measurement of creatinine levels is vital for patients with chronic kidney disease. Traditional creatinine level measurement requires invasive blood test which has several disadvantages like discomfort, anxiety, panic, pain, risk of infection ...
Parama Sridevi, Zawad Arefin, Sheikh Iqbal Ahamed   +2 more
doaj   +1 more source

Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Sensors, 2023
Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs.
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho   +4 more
doaj   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Source term inversion of nuclear accidents based on the Optuna–GRU model [PDF]

AIP Advances
Rapid and accurate inversion of radioactive source terms following a nuclear accident is critical for risk assessment and emergency response. Static neural networks are widely used for inversion because of their fast inference, but they struggle to ...
Chiyu Zhao, Sufen Li, Xingfu Cai, Yonggang Huo, Fei Wang   +4 more
doaj   +1 more source