A neurodynamics-based nonnegative matrix factorization approach based on discrete-time projection neural network

Zhang, Nian; Leatham, Keenan

doi:10.1007/s12652-019-01550-5

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Collaborative and in particular interdisciplinary research is needed to bring these advances to other fields and transform innovations into applications. The authors in [ 35 ] and [ 36 ] bear witness to the benefits of incorporating diversity to research teams. With authors with top degrees in civil engineering, computer science, and communications and graduate and undergraduate authors, these teams show that in order to push the science forward we need the help of everyone.…”

Section: Related Workmentioning

confidence: 99%

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

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Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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Section: Related Workmentioning

confidence: 99%

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

Self Cite

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Intrusion Detection Systems Using Machine Learning

Taylor,

Hussain,

Gogate

et al. 2023

Advances in Information Security

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Object Detection in Traffic Videos: A Survey

Ghahremannezhad

Shi²,

Liu

2023

IEEE Trans. Intell. Transport. Syst.

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Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.

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A neurodynamics-based nonnegative matrix factorization approach based on discrete-time projection neural network

Cited by 4 publications

References 21 publications

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Intrusion Detection Systems Using Machine Learning

Object Detection in Traffic Videos: A Survey

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