CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net

Swapna, M.; Sharma, Dr.Yogesh Kumar; Prasad, BMG

doi:10.35940/ijrte.f9532.038620

Cited by 25 publications

(7 citation statements)

References 10 publications

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“…In detail, the distinctive characteristics of each CNN architecture are listed in Table 1. Another study finding by Swapna [37], explained error rates in each CNN architecture and is in accordance with the results of this study.…”

Section: Discussionsupporting

confidence: 92%

Understanding of Convolutional Neural Network (CNN): A Review

Purwono

Ma’arif

Rahmaniar

et al. 2023

IJRCS

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The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

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Section: Discussionsupporting

confidence: 92%

Understanding of Convolutional Neural Network (CNN): A Review

Purwono

Ma’arif

Rahmaniar

et al. 2023

IJRCS

View full text Add to dashboard Cite

show abstract

“…For the movement of taking things, some movements may have a large amplitude, and the key points of the human body may be blocked, leading to inaccurate model recognition. In addition, it is worth noting that the recall rate of normal driving and Methods Accuracy e average time (ms) ResNet [6] 0.635 66 ObjDetection + ResNet [6] 0.854 120 PE [5] 0.756 78 ObjDetection + PE [5] 0.926 133 10 Security and Communication Networks…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…In recent years, with the rapid development of deep learning technology, more and more models have been proposed, including Alex Net, RCNN, Based-RCNN, VGG, GoogLe-Net, and ResNet [6].…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Security Classification Model of Driver’s Driving Behavior Based on V2X in IoT Networks

Dai

Zhong

et al. 2022

Security and Communication Networks

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Traffic accidents occur frequently in Internet of Things (IoT) safety system. Traffic accidents are largely caused by drivers’ unsafe driving behaviors in the process of driving. Aiming at the problem of low safety of real-time warning in driving, this paper proposes a model to detect driver behavior. Firstly, according to the driver target detection for positioning, combined with the Pose Estimation to identify the driver in the process of driving a variety of driving behaviors, at the same time, a rating model is built to score drivers’ driving behaviors. Then, by integrating the driver behavior model and evaluation rules, the system can give timely and active warning when the driver makes unsafe behavior in the process of driving. Finally, in the V2X scenario, feedback and presentation are given to users in the form of points. The experimental results show that, in the scenario of Internet of vehicles, the driving behavior rating model can well analyze and evaluate drivers’ driving behaviors, so that drivers can more accurately understand their abnormal driving behaviors and driving scores, which plays a significant role in IoT safety management.

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“…Deng and Yu [14] described a variety of deep networks for supervised and hybrid learning, such as the deep stacking network (DSN) and its derivatives. Schmidhuber [20] discussed all neural networks, from the first neural networks to the most recent convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) and their advancements [35].…”

Section: Supervised Learningmentioning

confidence: 99%

“…DBN is a collection of restricted Boltzmann machines (RBMs). A greedy learning method that optimises DBN weights with time complexity proportional to the size and depth of the networks lies at the heart of the DBN [35].…”

Section: What Role Does Deep Learning Play In Ai? (Evolution Of Deep ...mentioning

confidence: 99%

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Taye

2023

Computers

304

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In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

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CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net

Cited by 25 publications

References 10 publications

Understanding of Convolutional Neural Network (CNN): A Review

Understanding of Convolutional Neural Network (CNN): A Review

An Intelligent Security Classification Model of Driver’s Driving Behavior Based on V2X in IoT Networks

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

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