A Review on Basic Deep Learning Technologies and Applications

Patil, Tejashri; Pandey, Sweta; Visrani, Kajal

doi:10.1007/978-981-15-4474-3_61

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…Boundary Loss Function. The loss function measures the distance between the predicted and the expected output of the neural network [33,34]. The closer the predicted output is to the expected one, the smaller the value of the loss function.…”

Section: K-means++mentioning

confidence: 99%

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Niu

Chen

et al. 2023

Journal of Sensors

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With the rapid development of China’s printed circuit board industry, bare-board defect detection has high research and application values as an important factor in improving production quality. In this paper, a new detection method based on YOLOv5 is proposed to solve the balance problem of efficiency and performance in the task of circuit board defect detection. First, the k -means++ method is used to improve the location matching of the prior anchor boxes. Second, the Focal-EIOU loss function is used instead of GIOU to address the former’s degeneration issue. Finally, the ECA-Net module is added to enhance the sensitivity of the model to channel features. Experiments were conducted on a public defect dataset, and superior performance was achieved. The proposed method achieves 99.1% mean average precision at 86 frames per second. Compared with other advanced methods, the proposed method achieves the highest mean average precision value, and the detection speed allows real-time applications.

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Section: K-means++mentioning

confidence: 99%

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Niu

Chen

et al. 2023

Journal of Sensors

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“…AlexNet became very popular after winning a famous challenge dealing with visual recognition [13]. Since then, it has been used in a huge variety of applications [1]. The original architecture of this network is composed of eight layers with learning ability, where five are convolutional and three are fully-connected.…”

Section: Pre-trained Cnn Modelsmentioning

confidence: 99%

“…In the last decade, convolutional neural networks (CNNs) have evolved significantly. Indeed, they are being today applied in a broad variety of areas, such as sports, health, automotive and robotics, among others [1]. Within the field of medicine, these networks have been widely employed for automatic tasks, such as analysis and interpretation of ECG signals, classification of different kinds of arrhythmias, or biometric identification of subjects [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

Huerta¹,

Martínez-Rodrigo²,

Puchol³

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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In the last years, convolutional neural networks (CNNs) have become popular in ECG analysis, since they do not require pre-processing stages, nor specific pre-training. However, their ability for ECG quality assessment has still not been thoroughly assessed. Hence, this work introduces a comparison about the ability of several CNN algorithms to classify between high and low-quality ECGs. Taking advantage of the concept of transfer learning, five common pre-trained CNNs were analyzed, such as AlexNet, GoogLeNet, VGG16, ResNet18 and InceptionV3. They were fed with 2-D images obtained by turning 5 secondlength ECG segments into scalograms through a continuous Wavelet transform. To train and validate the algorithms, 1,168 noisy ECG intervals, along with other 1,200 ECG excerpts with sufficient quality for their further interpretation, were extracted from a public database. The obtained results showed that all CNNs provided mean values of accuracy between 89 and 91%, but notable difference in terms of computational load were noticed. Thus, AlexNet was the fastest algorithm, requiring notably less CPU usage and memory than the remaining methods. Consequently, this CNN exhibited the best trade-off between high-quality ECG identification accuracy and computational load, and it could be considered as the most convenient algorithm for ECG quality assessment.

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“…Deep learning is an artificial intelligence method used in areas such as object recognition, speech recognition, and natural language processing, and it uses multi-layer artificial neural networks. Today, many researchers in different fields such as big data [1][2][3][4][5][6], autonomous vehicles [7,8], handwritten character recognition [9,10], medical image processing, natural language processing [11,12], signature verification, voice and video recognition, are using deep learning method in their studies conducted in the most popular and challenging areas of the world [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of whether news on the Internet is real or fake by using deep learning methods and the TF-IDF algorithm

Korkmaz

Aydın

Yılmaz

2021

International Advanced Researches and Engineering Journal

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Internet use has become increasingly widespread nowadays. In addition, there is a significant increase in the amount of text content produced in digital media. However, the accuracy and inaccuracy of the news we read and the content produced in a large number are also unknown. In this study, classification and analysis of whether the news is real or not were done by using Deep Learning methods. For the English news, the data set created by Katharine Jarmul was used. The data set contained a total of 6336 news items. The distribution of this data set, which consisted of political and political news, was 50% fake and 50% real. The method used in text classification was Term Frequency -Inverse Document Frequency (TF-IDF). The classification was made with the data set used and 93.88% success and 6.12% error were obtained as a result of the analysis.

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A Review on Basic Deep Learning Technologies and Applications

Cited by 23 publications

References 9 publications

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

An Improved YOLOv5 Network for Detection of Printed Circuit Board Defects

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

Analysis of whether news on the Internet is real or fake by using deep learning methods and the TF-IDF algorithm

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