Results 191 to 200 of about 36,209 (267)

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source

Development and validation of a machine learning-based predictive model for carotid plaque in type 2 diabetes. [PDF]

Front Cardiovasc Med
Xing Y, Zhang L, Zhao Q.
europepmc   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

Majorbio Cloud 2026 Update: An End‐to‐End Solution for Proteomics Research

iMetaMed, EarlyView.
The recent upgrade of Majorbio Cloud facilitates a more streamlined and accurate research process, aiding researchers across the various proteomics subfields. Furthermore, it propels the advancement of multi‐layered, multi‐omics scientific investigations centered around proteins.
Caiping Shi, Chang Han, Jichen Han, Bing Yang, Yachen Dong, Jianhua Zhao, Binxu Liu, Rundong Fang, Wenyao Fu, Jiawen Ma, Meng Luo, Lixing Feng, Yitong Feng, Huimin Cui, Mengxi Li, Huasheng Huang, Xianglin Zhang, Kegang Yu   +17 more
wiley   +1 more source

Evaluation of Explainable Artificial Intelligence in IoT Intrusion Detection Systems Under DeepFool Adversarial Conditions. [PDF]

Sensors (Basel)
Munilla J, Khammas RM.
europepmc   +1 more source

Precision Mapping of Retinal Disease: Identification of Differential Progression Trajectories via Imaging Phenomics

iMetaMed, EarlyView.
Applying single‐cell RNA‐seq techniques to large‐scale clinical phenotypic data enables the discovery of differential disease progression trajectories and the construction of data‐driven progression scores. These can then be integrated into precision medicine studies to investigate the drivers of patient‐specific disease outcomes. ABSTRACT We propose a
Christian Anderson, Liam Scott, Irene Leung, Ferenc Sallo, Catherine Egan, Tunde Peto, The MacTel Consortium, Melanie Bahlo, Martin Friedlander, Simone Tzaridis, Lea Scheppke, Roberto Bonelli   +11 more
wiley   +1 more source

Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study. [PDF]

JMIR Form Res
Sun K.
europepmc   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +1 more source

Explainable machine-learning model for coronary artery disease diagnosis in non-AMI patients: integrating inflammatory and traditional risk factors. [PDF]

Open Heart
Yang K, Liu S, Wang C, Ni S, Bi L, Yang Y, Zhang M.   +6 more
europepmc   +1 more source

Metrological and Algorithmic Traceability of Machine Learning in Laboratory Medicine

Journal of Clinical Laboratory Analysis, EarlyView.
Machine learning is increasingly used in clinical laboratory medicine for test interpretation, outcome prediction, and laboratory operations, but its safe implementation requires traceability that covers both measurement science and data‐driven computation.
Qing Li, Mario Plebani
wiley   +1 more source