AIBullisharXiv – CS AI · Jun 47/10
🧠Researchers developed an explainable machine learning model using XGBoost to detect Alzheimer's disease stages from routine clinical assessments, achieving 98.2% accuracy on three-class classification (normal cognition, mild cognitive impairment, and Alzheimer's disease). The model uses SHAP analysis to provide interpretable feature importance, identifying clinical biomarkers like CDR Global and MMSE as key predictors.
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers developed FairMed-XGB, a machine learning framework that reduces gender bias in healthcare AI models by 40-72% while maintaining predictive accuracy. The system uses Bayesian optimization and explainable AI to ensure equitable treatment decisions in critical care settings.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers developed a hybrid machine learning model combining Transformers and XGBoost to forecast short-term electricity demand in New England, incorporating weather, calendar, and COVID-19 data. While the hybrid approach marginally outperformed a baseline model (2.05% MAPE vs 2.21%), statistical testing revealed the improvement is not significant, and an ablation study exposed how COVID-19 features caused overfitting to pandemic-era behavioral patterns that no longer applied.
AINeutralarXiv – CS AI · Jun 236/10
🧠Researchers developed an explainable AI framework combining GAN-based oversampling, Dragonfly Algorithm optimization, and XGBoost to predict mental health outcomes in drug-affected populations, achieving 94.17% accuracy. The model addresses class imbalance and interpretability challenges in clinical settings, identifying behavioral factors like sleep quality and emotional regulation as key predictive indicators.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose a hybrid machine learning architecture combining FT-Transformer neural networks with XGBoost gradient boosting to predict customer churn in banking and subscription services. The ensemble method achieves superior performance metrics (62.10% F1, 0.861 AUC-ROC) compared to baseline models while addressing critical challenges in class imbalance and probability calibration.
AINeutralarXiv – CS AI · Jun 95/10
🧠Researchers introduce IDS-Anta++, an enhanced machine learning framework that defends intrusion detection systems against adversarial attacks through ensemble learning and multi-layer defensive mechanisms. The system achieves over 99% detection accuracy on clean data while demonstrating improved robustness against sophisticated attacks like FGSM and ZOO on standard cybersecurity datasets.
AIBullisharXiv – CS AI · Jun 56/10
🧠Researchers present an XGBoost and SHAP-based intrusion detection framework for protecting U.S. critical infrastructure using explainable AI techniques. The study demonstrates how machine learning models combined with transparency mechanisms can enhance cybersecurity decision-making across energy, healthcare, transportation, and financial sectors.
AINeutralarXiv – CS AI · Jun 25/10
🧠A new study comparing machine learning approaches for churn prediction finds that traditional methods like Random Forests and XGBoost outperform advanced deep learning models in predictive accuracy, efficiency, and computational resource requirements. The research challenges the assumption that complex temporal models are always superior for time-series classification tasks in customer retention.
AIBullisharXiv – CS AI · Feb 276/105
🧠Researchers developed a lightweight intrusion detection system using XGBoost and explainable AI to detect Advanced Persistent Threats (APTs) at early stages. The system reduced required features from 77 to just 4 while maintaining 97% precision and 100% recall performance.
$APT
AINeutralarXiv – CS AI · Feb 274/105
🧠Researchers developed a machine learning framework to predict which clinical trials are likely to have high dosing error rates before the trials begin. The system analyzed 42,112 clinical trials and achieved 86.2% accuracy using a combination of structured data and text analysis, enabling proactive risk management in clinical research.
AINeutralarXiv – CS AI · Mar 34/105
🧠Researchers developed a new AI-powered surrogate model using XGBoost and CNNs to significantly reduce computational costs in phase field simulations for metal solidification processes. The adaptive uncertainty-guided approach achieves accurate predictions while requiring fewer expensive simulations and reducing CO2 emissions in additive manufacturing applications.
