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crossrefApplied Sciences2025-01-27Cited by 0

Generation of Penetrometric Profile of the Soil Applying Machine Learning to Measure While Drilling Data from Deep Foundation Machinery

Eduardo Martínez García, Marcos García Alberti, Antonio Alfonso Arcos Álvarez

The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results.

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crossrefApplied Sciences2025-08-05Cited by 35

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This systematic review examines how machine learning (ML) and generative AI (GenAI) have been integrated into learning analytics (LA) in higher education (2018–2025). Following PRISMA 2020, we screened 9590 records and included 101 English-language, peer-reviewed empirical studie…

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crossrefApplied Sciences2025-09-30Cited by 3

Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis

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Accurate and robust power quality disturbance (PQD) classification is critical for modern electrical grids, particularly in noisy environments. This study presents a comprehensive comparative evaluation of machine learning (ML) and deep learning (DL) models for automatic PQD iden…

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crossrefApplied Sciences2025-09-15Cited by 10

Malicious URL Detection with Advanced Machine Learning and Optimization-Supported Deep Learning Models

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This study presents a comprehensive comparative analysis of machine learning, deep learning, and optimization-based hybrid methods for malicious URL detection on the Malicious Phish dataset. For feature selection and model hyperparameter tuning, the Genetic Algorithm (GA), Partic…

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crossrefApplied Sciences2026-02-28

Comparative Analysis of Machine Learning and Deep Learning Models for Atrial Fibrillation Detection from Long-Term ECG

Lerina Aversano, Ilaria Mancino, Agostino Marengo, Chiara Verdone

Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal natur…

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crossrefApplied Sciences2026-01-03

Towards Intelligent Manufacturing: Machine Learning, Deep Learning, and Computer Vision for Tool Wear Estimation in Milling and Micromilling Processes

Vaibhav Joshi, Sameer Sayyad, Arunkumar Bongale, Satish Kumar, Vivek Warke, R. Suresh

In modern manufacturing, milling and micromilling processes play a central role in precision production. However, rapid wear of cutting tools often leads to sudden tool breakage, unplanned downtime, and part rejection. Maintenance is therefore essential to ensure efficiency, safe…

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crossrefApplied Sciences2025-07-19Cited by 10

Summarizing Recent Developments on Autism Spectrum Disorder Detection and Classification Through Machine Learning and Deep Learning Techniques

Masroor Ahmed, Sadam Hussain, Farman Ali, Anna Karen Gárate-Escamilla, Ivan Amaya, Gilberto Ochoa-Ruiz, et al.

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engageme…

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