Lung Cancer Detection Using Deep Convolutional Neural Network

Abidi, Syed Farhan Hyder; Sumukhi, T; Kumar, Harvendra; Santhosh, B.

doi:10.4018/ijoci.2021100102

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…From either the F6 or F7 layers, we can extract 4093-dimensional image features for the book page. The characteristics of the photograph taken using the F7 layer demonstrate superior retrieval performance [14]. Therefore, in this paper, the image after background segmentation and distortion correction is input into the trained VGG-F convolutional neural network, and the image feature codes of book pages are extracted from the F7 layer.…”

Section: Image Correctionmentioning

confidence: 99%

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Hu,

Yan,

Xie

2024

HighTech. Innov. J.

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Background: The development of technological devices has changed many facets of our lives, particularly the way we engage with information and learning. The advent of automated technology for identification has had a had a revolutionary effect on how we read and organize books within the context of books and data searches. It starts by solving the difficulties in analyzing photos of book pages by using methods such as distortion rectification and book separation. Objective: The research compares the effectiveness of the suggested method with traditional straight-line identification techniques using real-world testing. Methodology: The Skip-Gram model in Word2Vec is used to accurately represent spoken language, allowing word vectors to be generated and input data to be preprocessed for CNN. The results show that the methodology created regarding the present investigation works better than alternatives concerning accuracy and efficiency during line identification. Result:This work advances the field of book suggestion systems by presenting a strong and effective method that leverages CNNs. The findings demonstrate deep learning techniques may be used to optimize system recommendations and improve customer service and happiness in a variety of contexts. This technique creates a bridge between natural language processing and picture evaluation and opens up new possibilities for suggestion advancement along with user satisfaction. Doi: 10.28991/HIJ-2024-05-01-015 Full Text: PDF

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Section: Image Correctionmentioning

confidence: 99%

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Hu,

Yan,

Xie

2024

HighTech. Innov. J.

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A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation

Kulla

2022

International Journal of Distributed Systems and Technologies

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During recent years, the deep Q-Learning is used to solve different complex problems in different fields. However, Deep Q-Learning does not have a unified method for solving certain problems because different problems require specific settings and parameters. This paper proposes a Deep Q-Network with Experience Optimization for Atari’s “Space Invaders” environment called DQN-EO. Training and testing results are presented. The performance evaluation results show that while using the proposed algorithm the agent is better at avoiding enemy bullets by 37.7% (longer lifetime) and destroying enemy ships by 14.5% (higher score).

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Lung Cancer Detection Using Deep Convolutional Neural Network

Cited by 2 publications

References 2 publications

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

Automatic Recognition Technology of Library Books Based on Convolutional Neural Network Model

A Deep Q-Network Eith Experience Optimization (DQN-EO) for Atari's Space Invaders and Its Performance Evaluation

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