techxplore

Generative AI may cut costs in machine-learning systems, but it increases risks of cyberattacks and data leaks

Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

Mental math's shortcut—pupil dilation suggests people start solving before all numbers are in

People often solve simple arithmetic problems, such as basic addition, subtraction, multiplication or division, in their ...
Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
Scientific Research Publishing

Predicting Stock Price Movements Using Deep Learning Techniques ()

Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...
ascopubs.org

Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...
Frontiers

Enhancing disaster prediction with Bayesian deep learning: a robust approach for uncertainty estimation

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...
NKyTribune

CPE, DCBS launch new Community Learning Network to train health services professionals

The Kentucky Council on Postsecondary Education (CPE), in partnership with the Department for Community-Based Services (DCBS) and a consortium of postsecondary institutions, kicked off the Community ...
IEEE

Learning Bayesian Networks Under Local Differential Privacy

Abstract: Bayesian networks are widely used for causal discovery and probabilistic modeling across diverse domains including healthcare, multi-dimensional data analysis, environmental modeling, and ...