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Machine Learning Algorithms - A Review

Batta Mahesh • International Journal of Science and Research (IJSR) (2020)

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without being explicitly programmed. Learning algorithms in many applications that's we make use of dail...

OPENALEX Open Access DOI: https://doi.org/10.21275/art20203995

Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?

Navaneethakrishna Makaram, Sarvagya Gupta, Matthew Pesce, Jeffrey Bolton et al. • Algorithms (2023)

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images i...

DOAJ Open Access (DOAJ) DOI: 10.3390/a16120567

Precise diagnosis of Alzheimer’s disease based on sex-specific gray matter characteristics

Jiachen Chen, Jiachen Chen, Jiachen Chen, Kaiping Wang et al. • Frontiers in Neuroscience (2026)

IntroductionThere are notable sex differences in the gray matter of Alzheimer’s disease(AD) patients’ brains, but current evidence is insufficient to prove these differences aid diagnosis effectively.MethodsMultivariate analysis of variance...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnins.2026.1755938

High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength

Chao Xu, Dezhi Chen, Xiaofu Zhang, Qi Wang et al. • Nature Communications (2026)

Abstract High temperature Nb-Si based alloys face a critical challenge: achieving adequate room-temperature fracture toughness ( > 18 MPa·m1/2) for processing while maintaining high-temperature strength, properties that typically compete wi...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41467-026-75353-6

An introduction to quantum machine learning

Maria Schuld, Ilya Sinayskiy, Francesco Petruccione • Contemporary Physics (2014)

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT...

OPENALEX Open Access DOI: https://doi.org/10.1080/00107514.2014.964942

Machine-learning reprogrammable metasurface imager

Lianlin Li, Hengxin Ruan, Che Liu, Ying Li et al. • Nature Communications (2019)

Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we e...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-019-09103-2 PMC: 6403242

Machine Learning in Virtual Screening

James L. Melville, Edmund K. Burke, Jonathan D. Hirst • Combinatorial Chemistry & High Throughput Screening (2009)

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a partic...

OPENALEX DOI: https://doi.org/10.2174/138620709788167980

Machine Learning in Healthcare

Hafsa Habehh, Suril Gohel • Current Genomics (2021)

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, among...

OPENALEX Open Access DOI: https://doi.org/10.2174/1389202922666210705124359 PMC: 8822225

Machine learning in the Chinese stock market

Markus Leippold, Qian Wang, Wenyu Zhou • Journal of Financial Economics (2021)

We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for ...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.jfineco.2021.08.017

Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling

Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong et al. • Land (2025)

Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. While trajectory analysis is critical, existing studies stru...

DOAJ Open Access (DOAJ) DOI: 10.3390/land14050932

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

An Efficient and High-Speed Disturbance Detection Algorithm Design with Emphasis on Operation of Static Transfer Switch

USMAN, A., CHOUDHRY, M. A. • Advances in Electrical and Computer Engineering (2021)

Static Transfer Switch (STS) is required for high-speed transfer of essential load to the alternate power source when the main source fails due to power disturbance (PD). A fast and accurate PD detection method is required to ensure trans...

DOAJ Open Access (DOAJ) DOI: 10.4316/AECE.2021.02010

A review of machine learning in obesity

K. W. DeGregory, Patrick K. Kuiper, Thomas DeSilvio, James D. Pleuss et al. • Obesity Reviews (2018)

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine lea...

OPENALEX Open Access DOI: https://doi.org/10.1111/obr.12667 PMC: 8176949

A First Course in Machine Learning

Simon Rogers, Mark Girolami • Chapman & Hall/CRC eBooks (2016)

"A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations ...

OPENALEX DOI: https://doi.org/10.1201/9781315382159

Machine Learning and the Future of Cardiovascular Care

Giorgio Quer, Ramy Arnaout, Michael Henne, Rima Arnaout • Journal of the American College of Cardiology (2021)

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clin...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1016/j.jacc.2020.11.030 PMC: 7839163