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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23Cited by 0

PyEarthTools: Machine learning for Earth system science

Tennessee Leeuwenburg, Harrison Cook, Maxime Rio, Sanaa Hobeichi, Joel Miller, Gemma Mason, N S Ramanathan, John Pill, Stephen Haddad, Christian Stassen, Catherine de Burgh-Day, Ryan M. Holmes, Margarita Potokina, Jenya Bogacheva, Matthew James, Ben Sullivan, Edward Yang, Luke Hoffmann, Arthur Michael Pegios

Minor patch release tag supporting a minor fix to reshape.py plus recent refactoring changes. Additional testing is still under way but this version clearly designates the testing target.

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

BloomQGen: An Intelligent Question Paper Generation System Using Machine Learning and Bloom's Taxonomy

A. H Auti Pritesh Premnath Waghmare

Preparing examination question papers manually is a time-consuming and challenging task for educators. Faculty members must ensure balanced syllabus coverage, appropriate marks distribution, and proper assessment of students across different cognitive levels. Manual preparation o…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-25

# Artificial Intelligence-Enabled Quantification of Cube and Goss Textures in Polycrystalline Materials: A Comprehensive Review of Machine Learning, Deep Learning, and EBSD-Based Characterization Approaches

Sudhakar Geruganti

## ALTERNATIVE TITLES ### Alternative Title 1 (Comprehensive)**"AI-Driven Analysis of Cube {100}<001> and Goss {110}<001> Textures: Machine Learning, Deep Learning, and Generative Models for Crystallographic Texture Quantification in Metallurgical Engineering"** ### Alternative T…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-24

RAQA-Weather: A Machine Learning-Based Monitoring System for Iran

Abdullah Kaviani Rad

Executive Summary RAQA-Weather is a comprehensive weather intelligence system designed for Iran that combines real-time web scraping, machine learning-based spatial prediction, and interactive visualization. The system processes data from 17 synoptic stations across Iran and gene…

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openalexZenodo (CERN European Organization for Nuclear Research)2026-07-23

Stop Spatializing Time: Machine Learning Agents Should Learn Through Time, Not About Time

Teeratham Vitchutripop, Alyssa Quarles, Wei Zhang, Daniel Rakita

Modern machine learning systems are increasingly deployed in settings that require persistent interaction, adaptation, memory, and decision-making over time. Yet, most learning paradigms remove the temporal pressures faced by physically embedded agents: the world waits for comput…

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