Isiah Zaplana

Assistant Professor

Universitat Politècnica de Catalunya

Spain

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About

Isiah Zaplana is an Assistant Professor at Universitat Politècnica de Catalunya in Spain. His research areas focus on geometric algebra applications, machine learning in abiotic stress responses, and innovative approaches to waste management, particularly in electronics and materials recycling. Recent publications include works on symbolic geometric algebra routines and deep learning methodologies for quantitative analysis in materials science.

Articles (16)

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2025

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2025

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2023

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (9)

Demetrios Anglos

Professor

University of Crete

GREECE

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Jan Hejatko

Reserach Group Leader, Associated Professor

Masaryk University

CZECH REPUBLIC

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Josue Neftali Duarte Mejia

Assistant Professor

Universitat Politècnica de Catalunya

SPAIN

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Joan Lasenby

UNITED KINGDOM

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Dominique Van Der Straeten

BELGIUM

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Marketa Samalova

Masaryk University

CZECH REPUBLIC

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Toon Goedemé

professor

KU Leuven

BELGIUM

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Pavel Pořízka

Brno University of Technology

CZECH REPUBLIC

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Jan Rosell

Associate Professor

Universitat Politècnica de Catalunya

SPAIN

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Isiah Zaplana

Research Interests

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Articles (16)

Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis

Introducing Modeling, Analysis, and Control of Three-Phase Electrical Systems Using Geometric Algebra

Symbolic and User-friendly Geometric Algebra Routines ( SUGAR ) for Computations in Matlab

Evaluating the effectiveness of density-based sorting of plastics from WEEE and ELVs

Revisiting the Hansen Problem: A Geometric Algebra Approach

Smart Perception for Situation Awareness in Robotic Manipulation Tasks

Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

You Only Demanufacture Once (YODO): WEEE retrieval using unsupervised learning

Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Deep learning regression for quantitative LIBS analysis

Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches

Singularities of Serial Robots: Identification and Distance Computation Using Geometric Algebra

Simultaneous mass estimation and class classification of scrap metals using deep learning

Closed-form solutions for the inverse kinematics of serial robots using conformal geometric algebra

Forecasting global aluminium flows to demonstrate the need for improved sorting and recycling methods

A comparative assessment of resource-use criticality in advanced lithium-ion battery technologies

Collaborators (9)

Demetrios Anglos

Jan Hejatko

Josue Neftali Duarte Mejia

Joan Lasenby

Dominique Van Der Straeten

Marketa Samalova

Toon Goedemé

Pavel Pořízka

Jan Rosell

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