AI’s Real Bottleneck Isn’t Algorithms, It’s The Rare Earths Of Data

AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...
Quanta Magazine

Fed on Reams of Cell Data, AI Maps New Neighborhoods in the Brain

Machine learning is helping neuroscientists organize vast quantities of cells’ genetic data in the latest neurobiological cartography effort.
Gulf Business on MSN

Hitachi Energy’s Mohamed Almasry on AI data centres and grid stress

As AI data centres scale from megawatts to gigawatts, Hitachi Energy’s Mohamed Almasry explains why power grids are becoming ...

CIQ's NSS Module First to Achieve CAVP Certification for Post-Quantum Cryptography Algorithms

NSS with ML-KEM and ML-DSA algorithms passes lab testing and enters Modules in Process list, making Rocky Linux from CIQ one of the first Enterprise Linux distributions advancing FIPS-validated ...

Political division in the US surged from 2008 onward, study suggests

Divisions within the US population on social and political issues have increased by 64% since 1988, with almost all this coming after 2008, according to a study tracking polarization from the end of ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Devdiscourse

Data-driven logistics can cut medical risk in disaster zones

A new peer-reviewed study published in the journal Algorithms signals a major shift in how humanitarian logistics can be ...
IEEE

Parameter-Adaptive Border Peeling Clustering Algorithm

Abstract: Most clustering algorithms require setting one or more parameters, which rely on prior knowledge or are constantly adjusted based on external indicators. To address the issues of requiring ...
Johns Hopkins Newsletter

Sohini Ramachandran discusses population genetics and computational clustering algorithms

Family has always been important to those working in population genetics. When Sohini Ramachandran was a postdoc, the issue of relatives in a dataset causing inaccurate results was considered a major ...
Frontiers

SMCFO: a novel cuttlefish optimization algorithm enhanced by simplex method for data clustering

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing ...
GitHub

[ENH] Adding KModes and KPrototypes clustering algorithms

The workflow I want to enable is a seamless and native experience for clustering categorical and mixed data: This integrates categorical clustering directly into the robust and familiar scikit-learn ...
Scientific Research Publishing

Aggarwal, C.C. and Reddy, C.K. (2016) Data Clustering Algorithms and Applications. Chapman and Hall.

ABSTRACT: In the past decade, Internet Of Things (IOT) technology has become one of the fastest-growing and most widely used technologies and is rapidly becoming a basic feature of global civilization ...