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Benchmarking Supervised Classifiers for Concurrent Multidomain Dropout-Intention Attributions in Higher Education: Evidence from a Colombian Public University

Marieth Agnes Guillen-García, Osnamir Elias Bru-Cordero, Cristian David Correa-Álvarez • Big Data and Cognitive Computing (2026)

Student retention analytics often treats withdrawal as a single outcome, although students may attribute dropout intention to personal, socioeconomic, and academic pressures simultaneously. We benchmarked nine supervised classifiers for ide...

DOAJ Open Access (DOAJ) DOI: 10.3390/bdcc10080274

Interpretable machine learning for predicting compression index of clays using SHAP and gradient boosting models

Khaled Hamdaoui, Ali Benzaamia, Billal Sari Ahmed, Mohamed Elhebib Guellil et al. • Journal of Engineering and Applied Science (2025)

Abstract This study introduces a novel, interpretable machine learning framework for predicting the compression index (Cc) of clay soils by integrating three advanced gradient boosting algorithms—XGBoost, CatBoost, and LightGBM—with SHapley...

DOAJ Open Access (DOAJ) DOI: 10.1186/s44147-025-00727-4

Machine Learning in Compiler Optimization

Zheng Wang, Michael F. P. O’boyle • Proceedings of the IEEE (2018)

In the last decade, machine-learning-based compilation has moved from an obscure research niche to a mainstream activity. In this paper, we describe the relationship between machine learning and compiler optimization and introduce the main ...

OPENALEX Open Access DOI: https://doi.org/10.1109/jproc.2018.2817118

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

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

Neural Networks for Machine Learning

Richard F. Lyon • Cambridge University Press eBooks (2017)

In order for a digital neocortex to learn a new skill, it will still require many iterations of education, just as a biological neocortex does, but once a single neocortex somewhere and at some time learns something, it can share that knowl...

OPENALEX Open Access DOI: https://doi.org/10.1017/9781139051699.031

Finding Density Functionals with Machine Learning

John C. Snyder, Matthias Rupp, Katja Hansen, Klaus‐Robert Müller et al. • Physical Review Letters (2012)

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with ...

OPENALEX Open Access DOI: https://doi.org/10.1103/physrevlett.108.253002

Application of MRI-Based Radiomics in Preoperative Prediction of NF2 Alteration in Intracranial Meningiomas

Shuchen Sun, Shuchen Sun, Shuchen Sun, Leihao Ren et al. • Frontiers in Oncology (2022)

PurposeThis study aimed to investigate the feasibility of predicting NF2 mutation status based on the MR radiomic analysis in patients with intracranial meningioma.MethodsThis retrospective study included 105 patients with meningiomas, incl...

DOAJ Open Access (DOAJ) DOI: 10.3389/fonc.2022.879528

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

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

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

A metaheuristic with a neural surrogate function for Word Sense Disambiguation

Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari • Machine Learning with Applications (2022)

Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many task...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mlwa.2022.100369

Determinants of Tourism Demand Using Machine Learning Techniques

Musonera Abdou, Edouard Musabanganji, Herman Musahara • African Journal of Hospitality, Tourism and Leisure (2022)

The purpose of the current study was to determine factors affecting tourism demand using machine learning techniques. The results of different linear regression and random forest models on both the train and test sets were compared using RM...

DOAJ Open Access (DOAJ) DOI: https://doi.org/10.46222/ajhtl.19770720.256

Rapid Deployment of Deep Learning-Based Condition Monitoring for Rotating Machines

Aleksanteri Hamalainen, Aku Karhinen, Jesse Miettinen, Zacharias Dahl et al. • IEEE Access (2025)

Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3627885

Adversarial machine learning

Ling Huang, Anthony D. Joseph, Blaine A. Nelson, Benjamin I. P. Rubinstein et al. • ACM Workshop on Artificial Intelligence and Security (AISec) (2011)

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a...

OPENALEX DOI: https://doi.org/10.1145/2046684.2046692

Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study

Qingfeng Sun, Kai Zhang, Yuanlong Xu, Mengmei Luo et al. • BMC Infectious Diseases (2025)

Abstract Background HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identif...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12879-025-11998-w