Reset
Showing 20 of 146 academic articles

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Maria Grazia Puxeddu, Maria Grazia Puxeddu, Manuela Petti, Manuela Petti et al. • Frontiers in Systems Neuroscience (2021)

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across...

DOAJ Open Access (DOAJ) DOI: 10.3389/fnsys.2021.624183

Identifying urban villages using graph neural network and transfer learning in Southern Jiangsu, China

Zhenkang Wang, Nan Xia, Jiale Liang, Jiechen Wang • Geo-spatial Information Science (2026)

China’s rapid urbanization has led to the emergence of urban villages (UVs), whose governance is crucial for urban quality and built environment management. However, conventional identification methods rely on high-resolution remote sensing...

DOAJ Open Access (DOAJ) DOI: 10.1080/10095020.2026.2711549

Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study

Camille R. Reaux, Shelby A. Meche, Jordan M. Grider, Soundharya Dhanabal et al. • Frontiers in Education (2025)

Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationshi...

DOAJ Open Access (DOAJ) DOI: 10.3389/feduc.2024.1455173

A Comprehensive Survey on Graph Anomaly Detection With Deep Learning

Xiaoxiao Ma, Jia Xin Wu, Shan Xue, Jian Yu Yang et al. • IEEE Transactions on Knowledge and Data Engineering (2021)

Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of thes...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2021.3118815

Evaluating the Accuracy of Deep Learning Models and Dental Postgraduate Students in Measuring Working Length on Intraoral Periapical X-rays: An In vitro Study

R. S. Basavanna, Ishaan Adhaulia, N. M. Dhanyakumar, Jyoti Joshi • Contemporary Clinical Dentistry (2025)

Background: The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate ...

DOAJ Open Access (DOAJ) DOI: 10.4103/ccd.ccd_274_24

Deep learning: A philosophical introduction

Cameron J. Buckner • Philosophy Compass (2019)

Abstract Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been larg...

OPENALEX DOI: https://doi.org/10.1111/phc3.12625

Deep learning applications in ophthalmology

Ehsan Rahimy • Current Opinion in Ophthalmology (2018)

PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology. RECENT FINDINGS: Recent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the...

OPENALEX DOI: https://doi.org/10.1097/icu.0000000000000470

The unreasonable effectiveness of deep learning in artificial intelligence

Terrence J. Sejnowski A B • Proceedings of the National Academy of Sciences (2020)

Deep learning networks have been trained to recognize speech, caption photographs, and translate text between languages at high levels of performance. Although applications of deep learning networks to real-world problems have become ubiqui...

OPENALEX Open Access DOI: https://doi.org/10.1073/pnas.1907373117 PMC: 7720171

ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training

Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li et al. • Journal of Pharmaceutical Analysis (2025)

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.jpha.2025.101317

Distributed biconnectivity-preserving flocking with fast obstacle avoidance for UAV swarms

Jiaxin Li, Tong Duan, Peng Yi, Zhen Zhang et al. • Complex & Intelligent Systems (2026)

Abstract Obstacles in low-altitude environments force unmanned aerial vehicle (UAV) swarms to frequently alter their flight paths and communication links, which can easily cause network interruptions. It remains a significant challenge for ...

DOAJ Open Access (DOAJ) DOI: 10.1007/s40747-026-02343-y

Deep Learning on Graphs: A Survey

Ziwei Zhang, Peng Cui, Wenwu Zhu • IEEE Transactions on Knowledge and Data Engineering (2020)

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characte...

OPENALEX Open Access DOI: https://doi.org/10.1109/tkde.2020.2981333

Computational Linguistics and Deep Learning

Christopher D. Manning • Computational Linguistics (2015)

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the major Natural Language Processing (NLP) conferences. However, some pun...

OPENALEX Open Access DOI: https://doi.org/10.1162/coli_a_00239

Machine Learning with TensorFlow

Nishant Shukla • CERN Document Server (European Organization for Nuclear Research) (2018)

Summary Machine Learning with Tensor Flow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding Tensor Flow with Python. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub forma...

OPENALEX

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

Machine learning-assisted three-dimensional mapping of the facial soft tissues in Saudi Arabian adults with skeletal class I relationship

Nora Alhazmi, Rana Almazroa, Muhannad Alshehri, Saad Bin Shabib et al. • Journal of Orthodontic Science (2026)

OBJECTIVES: This study aimed to evaluate facial soft-tissue features in adults with skeletal Class I relationships using three-dimensional (3D) imaging and machine learning to identify sex-specific anthropometric patterns and provide refere...

DOAJ Open Access (DOAJ) DOI: 10.4103/jos.jos_111_25

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

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