Enhancing machine-learning interatomic potentials for advanced materials modeling

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 ...
IEEE

EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power ...

Building an AI Driven Portfolio Management System: From Machine Learning Basics to Advanced Implementation

Two important architectures are Artificial Neural Networks and Long Short-Term Memory networks. LSTM networks are especially useful for financial applications because they are designed to work with ...

Tenstorrent QuietBox tested: A high-performance RISC-V AI workstation trapped in a software blackhole

The $12K machine promises AI performance can scale to 32 chip servers and beyond but an immature software stack makes ...
IEEE

Attention-Driven Clustering of Programming Problem Difficulty Using Statistical and Machine Learning Techniques

Abstract: Programming is an important skill for advancement in computer science and information and communication technology (ICT). Online judge (OJ) systems are increasingly popular platforms in ...