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Showing 12 of 32 academic articles

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

Ya Zhang, Ravie Chandren Muniyandi, Faizan Qamar • Applied Sciences (2025)

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. T...

DOAJ Open Access (DOAJ) DOI: 10.3390/app15031552

On the Origin of Deep Learning

Haohan Wang, Bhiksha Raj • arXiv (Cornell University) (2017)

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

OPENALEX Open Access DOI: https://doi.org/10.48550/arxiv.1702.07800

Deep Learning in Microscopy Image Analysis: A Survey

Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu et al. • IEEE Transactions on Neural Networks and Learning Systems (2017)

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...

OPENALEX DOI: https://doi.org/10.1109/tnnls.2017.2766168

Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang et al. • Land (2023)

Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...

DOAJ Open Access (DOAJ) DOI: 10.3390/land12061125

Deep learning for electroencephalogram (EEG) classification tasks: a review

Alexander Craik, Yongtian He, Jose Luis Contreras-Vidal • Journal of Neural Engineering (2019)

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analyti...

OPENALEX cc-by DOI: https://doi.org/10.1088/1741-2552/ab0ab5

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

Recurrent Neural Networks Based Online Behavioural Malware Detection Techniques for Cloud Infrastructure

Jeffrey C. Kimmel, Andrew D. Mcdole, Mahmoud Abdelsalam, Maanak Gupta et al. • IEEE Access (2021)

Several organizations are utilizing cloud technologies and resources to run a range of applications. These services help businesses save on hardware management, scalability and maintainability concerns of underlying infrastructure. Key clou...

DOAJ Open Access (DOAJ) DOI: 10.1109/ACCESS.2021.3077498

Deep Learning

John D. Kelleher • The MIT Press eBooks (2019)

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars. Deep learning is an artificial intelligence technology that enables computer vi...

OPENALEX DOI: https://doi.org/10.7551/mitpress/11171.001.0001

Deep learning in bioinformatics

Seonwoo Min, Byunghan Lee, Sungroh Yoon • Briefings in Bioinformatics (2016)

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-th...

OPENALEX Open Access DOI: https://doi.org/10.1093/bib/bbw068

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...

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