Dual Architecture Deep Learning Based Object Detection System for Autonomous Driving

Mahmoud, Mahmoud M.; Nasser, Ahmed R.

doi:10.33103/uot.ijccce.21.2.3

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…However, it is utilized for screening, diagnosing, classification, and predictions [34]. Because of the discriminative representations of the CNN features, object detection employs deep CNN in order to extract features [35].…”

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

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The outbreak of the new coronavirus (COVID-19) has created a disaster worldwide and it became a very severe and acute disease. COVID-19 prevalence is rapidly increasing around the world. Deep learning (DL) technology had become a hot topic in the context of computing and is widely applied in various medical applications. These techniques have proven to be one of the effective tools for clinicians in the automatic diagnoses of COVID-19. The goal of the present paper is to provide an overview of recently developed systems based on DL techniques that use various medical imaging modalities such as Computer Tomography (CT) and Chest X-Rays (CXR). This review focuses on systems that had been developed for the diagnosis of COVID-19 with the use of the DL methods, as well as the well-known datasets that are utilized for the training of those networks. Finally, the researcher reviewed 58 research papers based on different medical images. Overall, this article aims to assist experts (medical or otherwise) and technicians to understand how the DL approaches are utilized in this context and the way that they can potentially be expanded to combat COVID-19 outbreaks.

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Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

A Survey of Deep Learning Applications for COVID-19 Detection Techniques Based on Medical Images

Saeed¹,

Oleiwi²

2022

ISI

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“…Due to its sequence-to-sequence nature, summarizing video, audio, pictures, and text is a difficult task [3], [4]. Deep learning methods have recently proven to be useful in a variety of domains, including image classification, summarization, machine translation, discourse identification, and text-to-speech production [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

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