Results 131 to 140 of about 36,209 (267)

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni, Md. Alif Sheakh, Mst. Sazia Tahosin, Md. Zahid Hasan, Taslima Ferdaus Shuva, Touhid Bhuiyan, Muhammad Ali Abdullah Almoyad, Nabil Anan Orka, Md. Tanvir Rahman, Risala Tasin Khan, M. Shamim Kaiser, Mohammad Ali Moni   +11 more
wiley   +1 more source

Interpretable Short‐Term Electric Load Forecasting

Advanced Intelligent Systems, EarlyView.
A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola, Giorgia Ghione, Vincenzo Randazzo, Vincenzo Franzitta, Marco Badami, Eros Pasero, Giansalvo Cirrincione   +6 more
wiley   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Survival prediction in colorectal cancer liver metastases using machine learning with SHAP-based interpretation. [PDF]

Front Oncol
Li N, Dong B, Liang Y, Liu L, Wang X, Zhang C, Hao S.   +6 more
europepmc   +1 more source

Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science

Advanced Intelligent Systems, EarlyView.
To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska, Joanna Neubauer, Monika Sztuder, Witold Taisner, Agnieszka Hryniewicka, Julia Kolodziejczyk, Marta Eliza Plonska‐Brzezinska, Dariusz Brzezinski   +7 more
wiley   +1 more source

Explainable machine learning reveals diverse yield-determining factors among Thai rice farmer cohorts: Implications for targeted agricultural support. [PDF]

PLoS One
Suriyalaksh M, Prommawin B, Hannanta-Anan P, Sreewongchai T, Promchote P, Nakasathien S.   +5 more
europepmc   +1 more source

Compositional Time–Temperature–Transformation and Post‐Processing Window Mapping of Epoxy Nanocomposites via Machine Learning

Journal of Applied Polymer Science, EarlyView.
Discrete DSC and rheological data are transformed into continuous compositional Time–temperature–transformation (TTT) diagrams through physics‐informed Gaussian Process Regression. The proposed framework quantitatively predicts post‐gel processing windows across temperature and nanofiller composition, providing a practical route for accelerated design ...
Otávio Bianchi, Dante A. Benvenutti, Leonardo B. Canto, Alessandra Lavoratti, Zainab Al‐Maqdasi, Sandro Campos Amico, Patrik Fernberg   +6 more
wiley   +1 more source

Explainable AI for mental health emergency returns: integrating large language models with predictive modeling. [PDF]

JAMIA Open
Ahmed A, Saleem M, Alzeen M, Birur B, Fargason RE, Burk BG, Alhassan A, Al-Garadi MA.   +7 more
europepmc   +1 more source

Computed Tomography‐Based Morphometric Analysis Reveals Intercondylar Notch Stenosis and Osteophytes Are Associated With Anterior Cruciate Ligament Injury

Arthroscopy, EarlyView.
Purpose To quantitatively assess intercondylar notch morphometrics using 3‐dimensional computed tomography reconstruction, evaluate their association with anterior cruciate ligament (ACL) injury in Asian populations, and investigate the prevalence of osteophytes in patients with ACL injury.
Xiaozhong Ma, WenMing Jiang, Chao Lu, Tongyue Ji, Su Yan, Heng Zhu, Xue He, Hao Shu, Lei Wang, Luning Sun   +9 more
wiley   +1 more source