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Showing 20 of 423 academic articles

Design, framework and benchmark of safety monitors for black-box classifiers

Fahad Ahmed Khokhar, Tommaso Zoppi, Luigi Cennini, Andrea Ceccarelli et al. • Scientific Reports (2026)

Abstract The last decade has seen a surging opportunity for developing autonomous systems (e.g., fully autonomous driving, computer-guided robotic surgery, mobile robots for inspections and surveillance, and manufacturing robots) to relieve...

DOAJ Open Access (DOAJ) DOI: 10.1038/s41598-026-45091-2

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

AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS

Dhurgham Ali Mohammed, Kalyani A. Patel • ICTACT Journal on Soft Computing (2025)

In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks su...

DOAJ Open Access (DOAJ) DOI: 10.21917/ijsc.2025.0518

Perspectives in machine learning for wildlife conservation

Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe et al. • Nature Communications (2022)

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently d...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41467-022-27980-y PMC: 8828720

A Topical Review of Graph Embedding in Graph Neural Networks

Willian Borges De Lemos, Lucas De Angelo Martins Ribeiro, Vanessa Telles Da Silva, Alessandro De Lima Bicho et al. • IEEE Access (2025)

Graph embeddings map graph-structured data into vector spaces for machine learning tasks. In Graph Neural Networks (GNNs), these embeddings are computed through message passing and support tasks such as node classification, link prediction ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2025.3643796

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

Aerosol Optical Depth Retrieval from MODIS Using a Physically Informed Machine Learning Framework

Tianchen Liang, Linqing Zou, Qiaoning He, Lin Sun • Remote Sensing (2026)

Retrieving aerosol optical depth (AOD) over land remains challenging because the relatively weak aerosol signal in top-of-atmosphere (TOA) observations must be separated from strong and spatially heterogeneous surface reflectance. Here, we ...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs18172862

Machine learning applications in epilepsy

Bardia Abbasi, Daniel M. Goldenholz • Epilepsia (2019)

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recogn...

OPENALEX Open Access DOI: https://doi.org/10.1111/epi.16333 PMC: 9897263

Integrated Bioinformatics Analysis and Experimental Verification of Immune Cell Infiltration and the Related Core Genes in Ulcerative Colitis

Zhao D, Qin D, Yin L, Yang Q • Pharmacogenomics and Personalized Medicine (2023)

Danya Zhao,1 Danping Qin,2 Liming Yin,3 Qiang Yang4 1The First School of Clinical Medicine of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2Department of Gastroenterology, The First Affiliated Hospital of Zheji...

DOAJ Open Access (DOAJ)

Machine learning for neuroimaging with scikit-learn

Alexandre Abraham, Fabián Pedregosa, Michael Eickenberg, Philippe Gervais et al. • Frontiers in Neuroinformatics (2014)

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series....

OPENALEX cc-by DOI: https://doi.org/10.3389/fninf.2014.00014 PMC: 3930868

Machine learning for precision medicine

Sarah J. MacEachern, Nils D. Forkert • Genome (2020)

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With t...

OPENALEX Open Access DOI: https://doi.org/10.1139/gen-2020-0131

Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire

Jongho Lee, Jiuk Shin, Jaewook Lee, Chorong Park et al. • Applied Sciences (2023)

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage...

DOAJ Open Access (DOAJ) DOI: 10.3390/app132111866

Overfitting and undercomputing in machine learning

Thomas G. Dietterich • ACM Computing Surveys (1995)

suggests a reasonable line of research: find algorithms that can search the hypothesis class better. Hence, there is been extensive research in applying second-order methods to fit neural networks and in conducting much more thorough search...

OPENALEX Open Access DOI: https://doi.org/10.1145/212094.212114

Machine Learning in Action

Peter de B. Harrington (2012)

SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to bui...

OPENALEX

Identification of arachidonic acid metabolism-related diagnostic markers in heart failure based on bioinformatics analysis and machine learning

Saiqing Chen, Chunxia Zhang, Yueting Yu • Frontiers in Cardiovascular Medicine (2025)

BackgroundHeart failure (HF) represents the terminal phase of multiple cardiovascular conditions and is associated with significant morbidity and mortality rates. Arachidonic acid (AA), an essential fatty acid, plays a crucial role in modul...

DOAJ Open Access (DOAJ) DOI: 10.3389/fcvm.2025.1625064

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Arfat Ahmad Khan, Muhammad Asif Nauman, Rab Nawaz Bashir, Rashid Jahangir et al. • IEEE Access (2022)

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2022.3206009

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

Michela Prunella, Roberto Maria Scardigno, Domenico Buongiorno, Antonio Brunetti et al. • IEEE Access (2023)

Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted ...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2023.3271748