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Showing 20 of 1,021 academic articles

The security of machine learning

Marco Barreno, Blaine A. Nelson, Anthony D. Joseph, J. D. Tygar • Machine Learning (2010)

Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We pre...

OPENALEX cc-by-nc DOI: https://doi.org/10.1007/s10994-010-5188-5

A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning

Subhabrata Sengupta, Rupayan Das, Satyajit Chakrabarti • Emitter: International Journal of Engineering Technology (2024)

Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, iden...

DOAJ Open Access (DOAJ) DOI: 10.24003/emitter.v12i2.855

mlr: Machine Learning in R

Bernd Bischl, Michel Lang, Lars Kotthoff, Patrick Schratz et al. (2013)

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensiti...

OPENALEX Open Access DOI: https://doi.org/10.32614/cran.package.mlr

Hybrid machine learning and 3D numerical modeling-based multi-objective optimization of geosynthetic-reinforced pile-supported embankments

Ammar Alnmr, Ammar Alnmr, Rashad Alsirawan, Haidar Hosamo et al. • Frontiers in Built Environment (2026)

Efficient and reliable design of Geosynthetic Reinforced Pile-Supported (GRPS) embankments on soft ground remains challenging due to the complex nonlinear soil–structure interaction and the high computational cost of repeated numerical anal...

DOAJ Open Access (DOAJ) DOI: 10.3389/fbuil.2026.1829950

PCPAm - A dataset of histopathological images of penile cancer for classification tasksZenodo

Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, João Dallyson Sousa de Almeida, Vandecia Rejane Monteiro Fernandes et al. • Data in Brief (2025)

Penile cancer has an incidence strongly linked to sociocultural factors, being more common in underdeveloped countries like Brazil, where it represents approximately 2% of cancers affecting men. This dataset was created to address the scarc...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.dib.2025.111823

Predicting Urban Quality of Life Using Machine Learning on Street Images and Personal Information with Statistical Feature Selection: A Comparative Study of Bangkok and London

Ployrada Suvarnakuta, Intouch Prakaisak, Pittipol Kantavat, Boonserm Kijsirikul et al. • Engineered Science (2025)

This study proposes a machine learning framework for predicting perceived urban quality of life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavi...

DOAJ Open Access (DOAJ) DOI: 10.30919/es1972

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling

Mafalda Reis Pereira, Mafalda Reis Pereira, Filipe Neves dos Santos, Fernando Tavares et al. • Frontiers in Plant Science (2023)

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant dis...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpls.2023.1242201

Design and Pilot Feasibility of a Low-Cost Wearable for Mexican Sign Language in Inclusive Higher Education

Juan Carlos Ramírez-Vázquez, Guadalupe Esmeralda Rivera-García, Marco Antonio Gómez-Guzmán, Marco Antonio Díaz-Martínez et al. • Technologies (2026)

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype el...

DOAJ Open Access (DOAJ) DOI: 10.3390/technologies14030189

Machine Learning–Based Wear Prediction of Recycled Magnesium Matrix Composites Reinforced With Ceramic Fibers

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam, Vivek Sivakumar et al. • Engineering Reports (2026)

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the...

DOAJ Open Access (DOAJ) DOI: 10.1002/eng2.70686

Machine learning in materials science

Jing Wei, Xuan Chu, Xiangyu Sun, Kun Xu et al. • InfoMat (2019)

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...

OPENALEX cc-by DOI: https://doi.org/10.1002/inf2.12028

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

Jian Liu, Fangyu Liu, Linbing Wang • Journal of Traffic and Transportation Engineering (English ed. Online) (2024)

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, whic...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jtte.2023.10.002

Introduction to Machine Learning

Yalın Baştanlar, Mustafa Özuysal • Methods in molecular biology (2013)

The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity ...

OPENALEX Open Access DOI: https://doi.org/10.1007/978-1-62703-748-8_7

Neurosurgery and artificial intelligence

Mohammad Mofatteh • AIMS neuroscience (2021)

Neurosurgeons receive extensive and lengthy training to equip themselves with various technical skills, and neurosurgery require a great deal of pre-, intra- and postoperative clinical data collection, decision making, care and recovery. Th...

OPENALEX cc-by DOI: https://doi.org/10.3934/neuroscience.2021025 PMC: 8611194

Multilevel Cognitive Machine-Learning-Based Concept for Artificial Awareness: Application to Humanoid Robot Awareness Using Visual Saliency

Kurosh Madani, Dominik M. Ramik, Cristophe Sabourin • Applied Computational Intelligence and Soft Computing (2012)

As part of “intelligence,” the “awareness” is the state or ability to perceive, feel, or be mindful of events, objects, or sensory patterns: in other words, to be conscious of the surrounding environment and its interactions. Inspired by ea...

DOAJ Open Access (DOAJ) DOI: 10.1155/2012/354785

PERMEPSY: a multicentre, randomized, double-blind proof-of-concept trial of personalized metacognitive training for adults with psychosis — a study protocol

Maria Lamarca, Maria Lamarca, Maria Lamarca, Maria Lamarca et al. • Frontiers in Psychiatry (2026)

BackgroundWhile psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1711659

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges

Luciellen da Costa Ferreira, Ian Carlos Bispo Carvalho, Lúcio André de Castro Jorge, Alice Maria Quezado-Duval et al. • Frontiers in Plant Science (2024)

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. ...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpls.2024.1387925