An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

Rezaei, Mahdi; Isehaghi, Morteza

doi:10.1109/aiar.2018.8769804

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…Some approaches to ZSL use non-linear compatibility functions. CMT [157] uses a two-layer neural network, similar to common MLP networks by [131] alongside the compatibility function. In UDA [71] a non-linear projection from feature space to semantic space (word vector and attribute) is proposed in an unsupervised domain adaptation problem based on regularised sparse coding.…”

Section: Label Embeddingmentioning

confidence: 99%

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Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Rezaei

Shahidi

2020

SSRN Journal

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The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection = recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few = one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection = recognition systems using ZSL.

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Section: Label Embeddingmentioning

confidence: 99%

“…CMT [157] uses a two-layer neural network, similar to common MLP networks by [131] that minimises the objective function…”

Section: Appendix a Additional Notes And A Review On Mathematical Fomentioning

confidence: 99%

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Rezaei

Shahidi

2020

SSRN Journal

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Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning

Vlahogianni

Ser

Κεπαπτσόγλου

et al. 2021

J. Big Data Anal. Transp.

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Address matching using machine learning methods: An application to register-based census

Rezaei Ghahroodi,

Ranji,

Rezaee

2024

SJI

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Today, most activities of the statistical offices need to be adapted to the modernization policies of the national statistical system. Therefore, the application of machine learning techniques is mandatory for the main activities of statistical centers. These include important issues such as coding business activities, address matching, prediction of response propensities, and many others. One of the common applications of machine learning methods in official statistics is to match a statistical address to a postal address, in order to establish a link between register-based census and traditional censuses with the aim of providing time series census information. Since there is no unique identifier to directly map the records from different databases, text-based approaches can be applied. In this paper, a novel application of machine learning will be investigated to integrate data sources of governmental records and census, employing text-based learning. Additionally, three new methods of machine learning classification algorithms are proposed. A simulation study has been performed to evaluate the robustness of methods in terms of the degree of duplication and purity of the texts. Due to the limitation of the R programming environment on big data sets, all programming has been successfully implemented on SAS (Statistical analysis system) software.

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An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

Cited by 3 publications

References 25 publications

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Model Free Identification of Traffic Conditions Using Unmanned Aerial Vehicles and Deep Learning

Address matching using machine learning methods: An application to register-based census

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