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Development of a Transfer Learning Technique for Rapid Adaptation of Thermal Compensation Models to Long-Term Machine Thermal Behavior Changes

Chia-Chin Chuang, Zheng-Wei Lin Chi, Tzu-Chien Kuo, Che-Jui Chang et al. • Machines (2026)

Structural aging and environmental changes associated with long-term operation can substantially modify the thermal behavior of machine tools, diminishing the accuracy of existing thermal compensation models. Traditional neural network appr...

DOAJ Open Access (DOAJ) DOI: 10.3390/machines14030309

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

Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation

Jennifer K. Briggs, J. N. Stroh, Soojin Park, Brandon Foreman et al. • npj Digital Medicine (2026)

Abstract Cerebral blood flow is essential for brain function and is governed by cerebral perfusion pressure and physiological mechanisms collectively referred to as cerebral vascular regulation (CVR). Direct measurement of cerebral blood fl...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41746-026-02600-x

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Kai Zhao, Yujia Shi, Hon-Cheong So • Pharmaceutics (2022)

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles fro...

DOAJ Open Access (DOAJ) DOI: 10.3390/pharmaceutics14020234

Trinity: In-Database Near-Data Machine Learning Acceleration Platform for Advanced Data Analytics

Ji-Hoon Kim, Seunghee Han, Kwanghyun Park, Soo-Young Ji et al. • IEEE Access (2024)

The ability to perform machine learning (ML) tasks in a database management system (DBMS) is a new paradigm for conventional database systems as it enables advanced data analytics on top of well-established capabilities of DBMSs. However, t...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2023.3347725

A novel machine learning-based model for predicting the transition fatigue lifetime in piston aluminum alloys

Mahmood Matin, Mohammad Azadi • International Journal of Lightweight Materials and Manufacture (2024)

The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm. For this purpose, an experimental dataset representing standard ISO specimens wit...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ijlmm.2024.04.004

An ensemble approach for imbalanced multiclass malware classification using 1D-CNN

Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy • PeerJ Computer Science (2023)

Dependence on the internet and computer programs demonstrates the significance of computer programs in our day-to-day lives. Such demands motivate malware developers to create more malware, both in terms of quantity and variety. Researchers...

DOAJ Open Access (DOAJ) DOI: 10.7717/peerj-cs.1677

TRIST: TREE RECOGNITION INTELLIGENT SYSTEM

Laura ONAC • Studia Universitatis Babes-Bolyai: Series Informatica (2019)

Plant recognition represents a challenging computer vision problem due to the great variations of shape and texture among plant organs, within the same species. This paper proposes a light-weight, but reasonably deep Convolutional Neural Ne...

DOAJ Open Access (DOAJ) DOI: 10.24193/subbi.2019.1.01

Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks

Santu Mondal, Sneha Ray, Aritra Acharyya, Rudra Sankar Dhar et al. • IEEE Access (2025)

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regim...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3567410

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

Cheng-Hong Yang, Chih-Hsien Wu, Kuei-Hau Luo, Huang-Chih Chang et al. • Ecotoxicology and Environmental Safety (2024)

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This st...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.ecoenv.2024.116885

Quantifying the incremental value of deep learning: Application to lung nodule detection.

Theodore Warsavage, Fuyong Xing, Anna E Barón, William J Feser et al. • PLoS ONE (2020)

We present a case study for implementing a machine learning algorithm with an incremental value framework in the domain of lung cancer research. Machine learning methods have often been shown to be competitive with prediction models in some...

DOAJ Open Access (DOAJ) DOI: 10.1371/journal.pone.0231468

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

Machine learning and applications in microbiology

Stephen J. Goodswen, Joel Barratt, Paul J Kennedy, Alexa Kaufer et al. • FEMS Microbiology Reviews (2021)

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence applica...

OPENALEX cc-by-nc DOI: https://doi.org/10.1093/femsre/fuab015 PMC: 8498514

Machine Learning in Earthquake Seismology

S. Mostafa Mousavi, Gregory C. Beroza • Annual Review of Earth and Planetary Sciences (2022)

Machine learning (ML) is a collection of methods used to develop understanding and predictive capability by learning relationships embedded in data. ML methods are becoming the dominant approaches for many tasks in seismology. ML and data m...

OPENALEX cc-by DOI: https://doi.org/10.1146/annurev-earth-071822-100323

Machine learning and medical education

Vijaya B. Kolachalama, Priya S. Garg • npj Digital Medicine (2018)

Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and othe...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41746-018-0061-1 PMC: 6550167

Machine Learning in Enzyme Engineering

Stanislav Mazurenko, Zbyněk Prokop, Jiřı́ Damborský • ACS Catalysis (2019)

Abstract Enzyme engineering plays a central role in developing efficient biocatalysts for biotechnology, biomedicine, and life sciences. Apart from classical rational design and directed evolution approaches, machine learning methods have b...

OPENALEX other-oa DOI: https://doi.org/10.1021/acscatal.9b04321

Foundations of Machine Learning

• The MIT Press eBooks (2012)

Foundations of Machine LearningSoon we will embark on a theoretical study of AdaBoost in order to understand its properties, particularly its ability as a learning algorithm to generalize, that is, to make accurate predictions on data not s...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.7551/mitpress/8291.003.0006

Memristor Synapse—A Device-Level Critical Review

Sridhar Chandrasekaran, Yao-Feng Chang, Firman Mangasa Simanjuntak • Nanomaterials (2026)

The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a m...

DOAJ Open Access (DOAJ) DOI: 10.3390/nano16030179

Design, framework and benchmark of safety monitors for black-box classifiers

Fahad Ahmed Khokhar, Tommaso Zoppi, Luigi Cennini, Andrea Ceccarelli et al. • Scientific Reports (2026)

Abstract The last decade has seen a surging opportunity for developing autonomous systems (e.g., fully autonomous driving, computer-guided robotic surgery, mobile robots for inspections and surveillance, and manufacturing robots) to relieve...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-45091-2

Machine learning-assisted three-dimensional mapping of the facial soft tissues in Saudi Arabian adults with skeletal class I relationship

Nora Alhazmi, Rana Almazroa, Muhannad Alshehri, Saad Bin Shabib et al. • Journal of Orthodontic Science (2026)

OBJECTIVES: This study aimed to evaluate facial soft-tissue features in adults with skeletal Class I relationships using three-dimensional (3D) imaging and machine learning to identify sex-specific anthropometric patterns and provide refere...

DOAJ Open Access (DOAJ) DOI: 10.4103/jos.jos_111_25