This year’s champion had a record win against the backdrop of a simulated market downturn.

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...

The field of artificial intelligence has reached a point where simply adding more data or increasing the size of a model is not the best way to make it more intelligent. For the past few years, we ...

NeuralStockTrader/ ├── src/ │ ├── data_layer/ # Data management & feature engineering │ │ ├── data_manager.py # OHLCV data fetching, technical indicators │ │ └── feature_engineer.py # Feature ...

Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Trading used to be about gut feelings and reading charts. Traders sat at desks watching screens, trying to spot patterns that meant prices would go up or down. That world exists still but machines can ...

Graph Neural Networks (GNNs) are reshaping AI by enhancing data interpretation and improving applications. Learn how GNNs are crucial in advancing machine learning models. Graph Neural Networks (GNNs) ...

Forbes contributors publish independent expert analyses and insights. Faculty member at Columbia University. Founder and CEO of OORT. Graph on a trader's computer screen, representing the concept of ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.