Results 161 to 170 of about 22,071 (254)

Shear strength of light weight concrete elements model based on deep neural network and COVID-19 optimization. [PDF]

Sci Rep
Shamseldin MA, Deifalla AF, Kontoni DN, Araby M.   +3 more
europepmc   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, Volume 75, Issue 4, Page 1574-1591, Otober 2026.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Introduction to Deep Neural Networks (DNNs)

openaire   +1 more source

Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 10, October 2026.
ABSTRACT Accurate prediction of time‐to‐event outcomes is essential for clinical decision‐making and model‐informed drug development (MIDD). Conventional survival models may inadequately represent complex longitudinal patient trajectories, time‐dependent treatments, and evolving biomarker or pharmacokinetic/pharmacodynamic (PK/PD) profiles.
Xinyi Liu, Shuai Shao, Dorothee Semiond, Malidi Ahamadi   +3 more
wiley   +1 more source

Advancing forward osmosis predictions: A deep learning-based surrogate modeling approach. [PDF]

J Sci Food Agric
Park HW, Kim WJ.
europepmc   +1 more source

Hybrid Quantum Kernel Support Vector Machine for Network Intrusion Detection Using NISQ‐Compatible Quantum Circuits

Advanced Quantum Technologies, Volume 9, Issue 10, October 2026.
A hybrid quantum kernel support vector machine is proposed to detect network intrusions with NISQ devices. The proposed framework combines classical preprocessing with fidelity‐based quantum feature mapping, achieving enhanced accuracy and robustness against cybersecurity threats and leveraging modern benchmark datasets, offering a glimpse of the ...
Mohammad Rafeek Khan, Malik Zaib Alam, Muneer Ahmad, Mohsin Iftikhar, Mohiuddin Ali Khan   +4 more
wiley   +1 more source

Dual-branch hierarchical fusion network for voice-based classification of type 2 diabetes mellitus using multi-vowel acoustic features. [PDF]

Front Med Technol
Jiang P, Zhang W, Li A, Li P.
europepmc   +1 more source

An Integrated Gradient Boosting Machine Approach With Fisher's Discriminant Analysis: Empirical Assessment on Five Benchmark Datasets and Simulations

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 5, October 2026.
ABSTRACT The aim of this study is to assess the impact of different dimensionality reduction techniques—specifically Principal Component Analysis (PCA) and Fisher's Linear Discriminant Analysis (LDA)—on the classification performance of Gradient Boosting Machines (GBM).
Giulio Enzo Donninelli, Pietro Giorgio Lovaglio   +1 more
wiley   +1 more source

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, Volume 34, Issue 6, Page 8559-8578, October 2026.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source

Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study. [PDF]

JMIR Cardio
Sedha J, Lipponen JA, Aho A, Jäntti H, Santala OE, Laitinen TP, Laitinen TM, Halonen J, Tarvainen MP, Seljola ES, Naukkarinen NS, Rantula O, Rissanen TT, Hartikainen JEK, Martikainen TJ.   +14 more
europepmc   +1 more source