How WeatherNext helped the National Hurricane Center better predict Hurricane Melissa’s historic landfall in Jamaica
Learn how our WeatherNext AI model help forecasters give communities unprecedented time to prepare ahead of the historic Hurricane Melissa.
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Adaptive Repayment Optimisation for SME Lending: A Stochastic Programming Framework with Generative AI Explanation
The Adaptive Repayment Optimisation Engine is introduced, a novel framework that applies constrained stochastic optimisation to the design of loan repayment schedules for small and medium-sized enterprises (SMEs) and contributes to the operations research literature by bridging stochastic programming, explainable AI, and financial regulation in a novel application domain.
An explainable generative AI framework for detecting low-rate API-based DDoS attacks in cloud environments
LLMs Leak Training Data Beyond Verbatim Memorization: Extraction via Membership Decoding
The Membership Decoding method is a plug-and-play replacement for standard decoding that requires only black-box token probabilities, and a new token-level membership inference method is proposed by leveraging likelihood from reference models, shifting the generation from the original token distribution to the member token distribution.
Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches
This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soil structures. These models were developed to enhance both predictive accuracy and model interpretability by incorporating state-of-the-art optimization algorithms and explainable machine learning frameworks. A comprehensive evaluation was conducted using 10-fold cross-validation, and the proposed models were benchmarked against previously developed AI models from literature, as well as traditional and semiempirical approaches such as Rankine, Coulomb, and K -stiffness. Among the proposed models, ASSA-MBM consistently achieved the best performance, recording the lowest testing root mean squared error (0.617), the highest correlation coefficient ( R = 0.918 ), and the highest reference index ( RI = 0.951 ). Additionally, the ORF model offers transparency by generating mathematical regression equations, which are crucial in geotechnical engineering.