MainReads

Discover Books. Explore Knowledge.

Home » Academic Articles » Artificial Intelligence (AI), Machine Learning, etc.

Artificial Intelligence (AI), Machine Learning, etc. Research Papers

Search and discover peer-reviewed scholarly literature, open-access articles, and research citations across DOAJ, PubMed Central, and OpenAlex.

Reset
Showing 20 of 337 academic articles

Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations

Charles Nickmilder, Anthony Tedde, Isabelle Dufrasne, Françoise Lessire et al. • Remote Sensing (2021)

Accurate information about the available standing biomass on pastures is critical for the adequate management of grazing and its promotion to farmers. In this paper, machine learning models are developed to predict available biomass express...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs13030408

Multidimensional Correlates of Childhood Stunting in India: A Spatial Machine Learning and Explainable AI Approach

Bhagyajyothi Rao, Md Gulzarull Hasan, Bandhavya Putturaya, Asha Kamath et al. • Stats (2026)

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Agai...

DOAJ Open Access (DOAJ) DOI: 10.3390/stats9020034

Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke

ChunYu Meng, FeiFei Zuo, Shuai Liu, YuLiang Ji et al. • Frontiers in Medicine (2026)

BackgroundThis study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web...

DOAJ Open Access (DOAJ) DOI: 10.3389/fmed.2026.1956859

The challenge of studying perovskite solar cells’ stability with machine learning

Paolo Graniero, Paolo Graniero, Mark Khenkin, Hans Köbler et al. • Frontiers in Energy Research (2023)

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technolog...

DOAJ Open Access (DOAJ) DOI: 10.3389/fenrg.2023.1118654

Directional Gaussian Mixture Models of the Gut Microbiome Elucidate Microbial Spatial Structure

Amey P. Pasarkar, Tyler A. Joseph, Itsik Pe’er • mSystems (2021)

ABSTRACT The gut microbiome is spatially heterogeneous, with environmental niches contributing to the distribution and composition of microbial populations. A recently developed mapping technology, MaPS-seq, aims to characterize the spatial...

DOAJ Open Access (DOAJ) DOI: 10.1128/mSystems.00817-21

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Jan Pišl, Marc Rußwurm, Lloyd Haydn Hughes, Gaston Lenczner et al. • Environmental Research Letters (2024)

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation ...

DOAJ Open Access (DOAJ) DOI: 10.1088/1748-9326/ad44b2

Exploring the application and challenges of fNIRS technology in early detection of Parkinson’s disease

Pengsheng Hui, Yu Jiang, Jie Wang, Congxiao Wang et al. • Frontiers in Aging Neuroscience (2024)

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder that significantly benefits from early diagnosis for effective disease management and intervention. Despite advancements in medical technology, there remains a cri...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnagi.2024.1354147

Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

Tan Yan Lin, Pang Ying Han, Ooi Shih Yin, Khoh Wee How et al. • Journal of Engineering Technology and Applied Physics (2023)

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple...

DOAJ Open Access (DOAJ) DOI: 10.33093/jetap.2023.5.2.12

When more is not always better: exercise dose and children’s cognitive development from an interpretable machine learning perspective

Wenbing Zhu, Weiqiang Zhang, Liming Yong • Frontiers in Psychiatry (2026)

BackgroundExercise intervention has been associated with cognitive development in children. However, current study methods have not clarified the potential nonlinear association between exercise dosage and cognitive ability, and interpretab...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1908580

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

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

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

Unlocking pseudocapacitors prolonged electrode fabrication via ultra-short laser pulses and machine learning

Kavian Khosravinia, Amirkianoosh Kiani • iScience (2023)

Summary: Pseudocapacitors outperform lithium-ion batteries in terms of charging rate and power density. However, their electrode manufacturing procedures are prolonged and environmentally unfriendly, posing a research challenge. To address ...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.isci.2023.106438