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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 +11 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 +2 more
wiley +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 +7 more
wiley +1 more source

Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis

Scientific Reports
The accurate classification of obesity is essential for public health and clinical decision-making. Traditional anthropometric measures such as body mass index (BMI) have limitations in differentiating between fat and lean mass.
Rodrigo Yáñez-Sepúlveda +20 more
doaj +1 more source

Playing in the Dark: Invisible Chess as a Laboratory for Strategic AI

AI &Innovation, EarlyView.
This paper shows that strategic AI evaluated on perfect‐information benchmarks can be brittle in real adversarial settings. By using invisible chess as a benchmark for hidden state and deception, it argues for stricter testing, human oversight, and more cautious governance of high‐stakes AI systems.
Paolo Ciancarini
wiley +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 +9 more
wiley +1 more source

Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods

Discover Applied Sciences
Traditionally, ASR expansion is determined using experimental and finite element method (FEM) based numerical modelling. However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging.
Yasitha Alahakoon +5 more
doaj +1 more source

Identifying Systemic Lupus Erythematosus From Serum Proteomic Profiles Using Machine Learning and Genetic Risk Stratification

Arthritis &Rheumatology, EarlyView.
Objective Proteome‐wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profiles. Methods Patients with lupus and individuals with other autoimmune diseases in the UK Biobank were included.
Mehmet Hocaoǧlu, Jishnu Das, Amr H. Sawalha +2 more
wiley +1 more source

Machine Learning to Predict Remission Between 6 and 24 Months in Rheumatoid Arthritis: Insights From JAK, an International Registry Collaboration

Arthritis &Rheumatology, EarlyView.
Objective To develop, externally validate, and simplify a machine learning model to predict remission between 6 and 24 months in patients with rheumatoid arthritis (RA) initiating tumor necrosis factor inhibitors, JAK inhibitors, interleukin‐6 inhibitors, abatacept, or rituximab using data from 11 international registries in the JAK‐pot collaboration ...
Zubeyir Salis +22 more
wiley +1 more source

Inferring Rheumatoid Arthritis Disease Activity Status From the Electronic Health Records Across Health Systems

Arthritis &Rheumatology, EarlyView.
Objective Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. The inconsistent availability of data on disease activity in real‐world electronic health records (EHRs) data has limited the ability to generate real‐world evidence (RWE).
David Cheng +34 more
wiley +1 more source

machine learning
fos: computer and information sciences
xgboost

machine learning cs.lg
explainable artificial intelligence
interpretability

explainability
shap
artificial intelligence