Economists from HSE University have developed a neural network model that can predict the onset of a short-term stock market crisis with over 83% accuracy, one day in advance. The model performs well even on complex, imbalanced data and incorporates not only economic indicators but also investor sentiment. The paper by Tamara Teplova, Maksim Fayzulin, and Aleksei Kurkin from the Centre for Financial Research and Data Analytics at the HSE Faculty of Economic Sciences has been published in Socio-Economic Planning Sciences.

Researchers have shown that patients with benign vocal fold lesions and laryngeal cancer can be distinguished through acoustic features of the voice, especially the harmonic-to-noise ratio and its variation within speech. Now that the proof-of-principle has been established, this task can be fed into machine learning algorithms, to build AI applications that triage patients at risk for laryngeal cancer based on their voice.

A real-time detection algorithm GBiDC-PEST for four tiny pests on mobile devices was developed. Model size was reduced by 80% while maintaining accuracy (>80%) in GBiDC-PEST. The GBiDC-PEST optimization algorithm and its mobile deployment offer a robust technical framework for the rapid, onsite identification and localization of tiny pests. This advancement provides valuable insights for effective pest monitoring, counting, and control in various agricultural settings

Researchers demonstrate that Synthetic Biological Intelligence (SBI) systems react faster, more effectively to stimuli than state-of-the-art RL (reinforcement learning) algorithms. To access these properties, Cortical Labs - which led the research - built the world’s first biological computer, the CL1. With the establishment of a new approach, Bioengineered Intelligence (BI), researchers will seek to establish that engineered biological systems can surpass natural physiological limits, unlocking capabilities beyond those previously demonstrated