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Fatigue Monitoring Through Wearables: A State-of-the-Art Review

Neusa R. Adão Martins, Neusa R. Adão Martins, Simon Annaheim, Christina M. Spengler et al. • Frontiers in Physiology (2021)

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems...

DOAJ Open Access (DOAJ) DOI: 10.3389/fphys.2021.790292

Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming

Giuseppe Altieri, Sabina Laveglia, Mahdi Rashvand, Francesco Genovese et al. • Applied Sciences (2025)

This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectr...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15116233

MATCHA, a novel regional hydroclimate-chemical reanalysis: System description and evaluation

C. Roychoudhury, R. Kumar, C. He, W. Y. Y. Cheng et al. • Earth System Science Data (2026)

We present MATCHA (Model for Atmospheric Transport and Chemistry in Asia), a 17-year (2003–2019) regional hydroclimate-chemical reanalysis for Asia (58–140° E, 4– 40° N) at 12 km resolution. MATCHA couples the Weather Research and Forecasti...

DOAJ Open Access (DOAJ) DOI: 10.5194/essd-18-5209-2026

Machine learning in manufacturing: advantages, challenges, and applications

Thorsten Wuest, Daniel Weimer, Christopher Irgens, Klaus‐Dieter Thoben • Production & Manufacturing Research (2016)

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means av...

OPENALEX cc-by DOI: https://doi.org/10.1080/21693277.2016.1192517

Integrating InVEST and machine learning to model mangrove habitat degradation trend in in Northern Persian Gulf, Iran

Mohamad Kazemi, Atefeh Jafarpoor, Youssef Ahmadi, Raoof Mostafazadeh • Scientific Reports (2026)

Abstract The expansion of human activities and environmental changes in coastal desert and semi-desert ecosystems significantly impact habitat quality. This study used remote sensing data on the Google Earth Engine (GEE) platform to generat...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-47305-z

Machine Learning for Climate Precipitation Prediction Modeling over South America

Juliana Aparecida Anochi, Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho • Remote Sensing (2021)

Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs13132468

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton et al. • BMC Bioinformatics (2018)

Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D stru...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12859-018-2381-1

Synergistic multi-altimeter for estimating water level in the coastal zone of Beibu Gulf using SEL, ALES + and BFAST algorithms

Jiaoling Qin, Jiaoling Qin, Sunzhe Li, Sunzhe Li et al. • Frontiers in Marine Science (2023)

Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal usin...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmars.2022.1113387

Perspectives in machine learning for wildlife conservation

Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe et al. • Nature Communications (2022)

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently d...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-022-27980-y PMC: 8828720

Machine Learning into Metaheuristics

El‐Ghazali Talbi • ACM Computing Surveys (2021)

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...

OPENALEX Open Access DOI: https://doi.org/10.1145/3459664

Machine Learning Mastery

Naga Ramesh Palakurti, Nageswararao Kanchepu • Advances in systems analysis, software engineering, and high performance computing book series (2024)

This chapter delves into the core principles of machine learning, offering practical insights for effective data processing. From foundational concepts to advanced techniques, the narrative unfolds as a comprehensive guide for harnessing th...

OPENALEX DOI: https://doi.org/10.4018/979-8-3693-2909-2.ch002

Interpretable Intersection Control by Reinforcement Learning Agent With Linear Function Approximator

Somporn Sahachaiseree, Takashi Oguchi • IET Intelligent Transport Systems (2025)

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximat...

DOAJ Open Access (DOAJ) DOI: 10.1049/itr2.70034

Hands-On Machine Learning with R

Brad Boehmke, Brandon Greenwell • Chapman & Hall/CRC eBooks (2019)

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process a...

OPENALEX DOI: https://doi.org/10.1201/9780367816377

Calibrated Machine Learning with Temporal Leakage Controls for Rare-Event Forecasting of Port Activity Anomalies: Evidence from Selected African Countries and Indonesia

Tomasz Rokicki, Piotr Bórawski, Aneta Bełdycka-Bórawska, Bogdan Klepacki • Applied Sciences (2026)

Port activity anomalies are rare but may signal operational constraints relevant to supply chains. The aim of the study was to develop and empirically evaluate a calibrated machine learning framework for forecasting rare, algorithmically de...

DOAJ Open Access (DOAJ) DOI: 10.3390/app16189017

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

Ployrada Suvarnakuta, Intouch Prakaisak, Pittipol Kantavat, Boonserm Kijsirikul et al. • Engineered Science (2025)

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

DOAJ Open Access (DOAJ) DOI: 10.30919/es1972

lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses

Ahmed Medhat Zayed, Arne Janssens, Pavlos Mamouris, Nicolas Delvaux • BMC Medical Informatics and Decision Making (2024)

Abstract Background The integrity of clinical research and machine learning models in healthcare heavily relies on the quality of underlying clinical laboratory data. However, the preprocessing of this data to ensure its reliability and acc...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12911-024-02652-7