Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...
PNAS

Demonstrating real advantage of machine learning–enhanced Monte Carlo for combinatorial optimization

In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...
Law

Output vs. Process: When Attorneys Should — and Shouldn't — Use AI

Every legal task produces two things: output and process. Firms that compete on output alone will find themselves in a race to the bottom against tools that do not bill by the hour and never will. The ...
Computer Weekly

Glasgow researchers use machine learning to build network digital twin

Researchers at the University of Glasgow have developed a new way to test networks, which they claim is 25,000 times faster than traditional approaches. Shenjia Ding, a research student at the ...
Frontiers

Hybrid optimization of interpretable ensemble machine learning for petrophysical property prediction from well logs

Unconventional reservoirs, including shale gas, tight oil, and coalbed methane formations, have emerged as vital contributors to global energy security, accounting for a substantial portion of the ...
IEEE

Predictive Model for Cantilever Beam Optimization Using Machine Learning

This paper introduces an innovative approach to optimizing the designs of cantilever beams for sensor applications. It combines machine learning techniques with quasi-Newton optimization methods.
IEEE

Closed-Loop Parameter Optimization for Robotic Machining Using Physics-Informed Machine Learning and Multiobjective Optimization

Abstract: In practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck ...
Scientific Research Publishing

Comparative Research in Sentiment Analysis Using Machine Learning Technique ()

In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a ...
C&EN

Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
GitHub

McPortfolio - LLM-Driven Portfolio Optimization

This project allows users to work with advanced portfolio optimization using natural language, without writing code. It provides 9 specialized MCP tools covering everything from classic mean-variance ...