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Author: search.filters.author.Ponce, HiramStart date: 2020

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    Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
    (Springer Nature Switzerland, 2026)
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    Gomez-Coronel, Sandra L.
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    Renza Torres, Diego
    This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions. Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions. This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development. ©The authors ©Springer.
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    Orthosis Control Based on Electromyographic Signals and Machine Learning
    (Springer Nature Switzerland, 2025)
    Escobedo-Gordillo, Andrés
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    Díaz, Fernando
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    Villa, Jesús
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    Sepúlveda, Miguel
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    García-Casas, Sebastián
    The human hand is indispensable for daily activities, and those who suffer from dysfunction due to strokes or accidents often require therapy to improve their condition. This study has developed a hand orthosis that uses surface Electromyographic (sEMG) signals and machine learning to address therapeutic needs and improve the quality of life for individuals with reduced motor skills in their hands and/or wrists. While current orthoses meet therapy requirements, they do not incorporate machine learning (ML) or sEMG sensors to optimize performance and accessibility. This chapter describes a remote-controlled, electro-mechanical orthosis that can replicate six basic movements of the human hand using three sEMG channels and ML. Our dataset of 14,400 samples, each labeled with a hand gesture, was generated by eight participants. The orthosis is comfortable and customizable for different users, as shown in prototype testing. The convolutional neural network (CNN) used achieves an accuracy of 90.38% with an inference time of 1.515 ms. Therefore, this orthosis system has significant potential for further development and practical application in patients who require such intervention. ©The authors ©Springer.
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    Modular IoT Hydroponics System
    (MDPI AG, 2025-10-31)
    Manlio Fabio Aranda Barrera
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    <jats:p>Hydroponics offers a promising alternative to soil-based agriculture, enabling higher yields, resource efficiency, and improved crop quality. This study compares traditional hydroponic setups with systems enhanced through the Internet of Things (IoT) framework using the Nutrient Film Technique and a proportional–integral controller, focusing on growth performance and environmental control. Systems incorporating Internet of Things technology achieved a growth rate of 0.94 cm/day versus 0.16 cm/day for conventional setups, due to precise water temperature control, optimized lighting, data acquisition, targeted nutrients, and reduced pest incidence. The integration of Industry 4.0 principles further enhances sustainable production and resource management. Statistical validation under diverse conditions is recommended. Future work will add environmental sensors, refine mechanical design, and explore machine learning for adaptive control, highlighting the potential of Internet of Things–based hydroponics to transform agriculture through intelligent, efficient, and eco-friendly cultivation.</jats:p>
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    Design and Prototyping of a Bio-inspired Robotic Tail
    (Springer Nature Switzerland, 2025-11-18)
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    Alexia Ramírez
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    Predicting Substance Addiction in University Students: A DSM-5-Guided Machine Learning Model
    (Springer Nature Switzerland, 2025-10-24)
    Pablo González Bustamante
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    Pre-trained Models for Grammatical Error Correction in Healthcare-Specific Text
    (Springer Nature Switzerland, 2025-10-24)
    González Mora José Guillermo
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    Metaheuristic Strategy in Automatic Robotics Navigation for Patient Care in Hospitals
    (Springer Nature Switzerland, 2025-10-11)
    Irvine J. Monroy-Rueda
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    Automatic Robotics Medication Delivery System: The ANDIS Case Study
    (Springer Nature Switzerland, 2025-10-11)
    Pablo Carbajal
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    Ethan Cobb
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    César Hernández
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    Alfredo Mejía
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    Lucía Menchaca
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    Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications
    (Springer Science and Business Media Deutschland GmbH, 2026)
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    Gomez-Coronel, Sandra L.
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    Renza Torres, Diego
    This book presents a multidisciplinary collection of machine learning approaches applied to the biomedical field, with a focus on computer-aided diagnostic systems, healthcare support tools, biological applications, and sustainable development in health. Computer-aided diagnostic systems leverage machine learning methods to support medical diagnosis, while healthcare support tools, biological applications, and sustainability-oriented studies aim to improve patients’ quality of life, propose innovative solutions to biological challenges, and incorporate sustainability into healthcare processes. The contributions in this book offer readers a holistic view of new methods used to address current biomedical challenges in medicine, biology, and health sciences. By applying artificial intelligence algorithms, mathematical theories, and emergent systems, these works demonstrate how such approaches can improve specific problems or propose innovative solutions. This book is valuable for readers interested in recent advances in machine learning for diagnostic systems, healthcare applications, biological research, and sustainability-related issues. ©The authors ©Springer.