Science – Page 129 – Minority Mindset

Decoding Cyber Threats with AI’s Explanations

10.11.2025 by qfx

Retrieval-Augmented Generation (RAG) systems, when applied to a Blockchain-based Cyber Threat Intelligence (CTI) dataset, demonstrate a capacity to match answers, though the inherent complexity suggests even elegant solutions will eventually succumb to the realities of production-level data.

New research explores how advanced AI can not only identify cyber threats, but also clearly explain why.

Predicting Road Risk with Probabilistic Deep Learning

10.11.2025 by qfx

The assessment of crash risk along the San Antonio River Walk revealed that a probabilistic model—by prioritizing nuanced spatial coherence and superior recall of historical fatal crashes—generated a more realistic safety evaluation than a baseline model prone to abrupt fluctuations and missed critical locations.

A new approach uses the full power of probability to more accurately assess crash likelihood on roadways.

Predicting Heart Risk in Diabetes: A New Era of Accuracy

10.11.2025 by qfx

Machine learning and deep learning techniques are significantly improving the prediction of cardiovascular disease in diabetic patients.

Predicting Carbon Prices: A New Approach to Forecasting Emissions Markets

10.11.2025 by qfx

Multiple forecasting models were evaluated, revealing varying degrees of accuracy in predicting carbon price fluctuations.

A novel forecasting framework combining signal processing and deep learning dramatically improves the accuracy of EU carbon price predictions.

Beyond the Algorithm: Making Credit Decisions Transparent

10.11.2025 by qfx

The neural network, despite its complexity, demonstrates a predictable, instance-by-instance rationale, suggesting that even sophisticated models ultimately rely on discernible, albeit potentially opaque, logic when making predictions.

New research details a practical framework for evaluating how easily we can understand the reasoning behind AI-powered credit risk assessments.

Predicting Grid Resilience in a Renewable Future

10.11.2025 by qfx

Modal features extracted from time-series data via Dynamic Mode Decomposition are mapped onto evolving dynamic adjacency matrices, enabling a recurrent network to forecast probabilities of system instability—a process inevitably strained by the complexities of real-world application despite its theoretical elegance.

A new approach to forecasting power system stability leverages the power of neural networks and dynamic systems analysis.

Mapping Landslides with AI: A New Approach to Hazard Prediction

10.11.2025 by qfx

Landslide mapping has evolved from expert-driven analysis to geospatial foundation models—leveraging self-supervised learning and fine-tuning—offering scalable, adaptable, and data-efficient solutions poised to disrupt the reinforcing cycle of climate-driven landslides and escalating community vulnerability through proactive risk management and transferable knowledge.

Geospatial foundation models demonstrate superior performance in landslide mapping, even with limited data and across diverse terrains.