scholarly journals Author Correction: A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tao Lu ◽  
Baokun Han ◽  
Lipin Chen ◽  
Fanqianhui Yu ◽  
Changhu Xue
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Practical Applications

scholarly journals A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tao Lu ◽  
Baokun Han ◽  
Lipin Chen ◽  
Fanqianhui Yu ◽  
Changhu Xue
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Practical Applications ◽  
High Classification Accuracy ◽  
Proposed Model ◽  
Real World Applications ◽  
New Ideas ◽  
High Level

AbstractA generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.

scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

The Animal Classification: An Evaluation of Different Transfer Learning Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 27-31
Author(s):  
Ken-ji Ee ◽  
Ahmad Fakhri Bin Ab. Nasir ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Nur Hafieza Ismail
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Classification Accuracy ◽  
Validation Technique ◽  
Search Approach ◽  
Fold Cross Validation

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.

scholarly journals Developing intelligent medical image modality classification system using deep transfer learning and LDA

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mehdi Hassan ◽  
Safdar Ali ◽  
Hani Alquhayz ◽  
Khushbakht Safdar
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Medical Image ◽  
Image Modality

Multiaxial classification in child and adolescent mental health – a reaffirmation of benefit and practical applications

2021 ◽  
pp. 103985622110092
Author(s):  
Mark Mayall ◽  
Brett McDermott ◽  
Raja Sadhu ◽  
Cortney Husodo
Keyword(s):  
Mental Health ◽  
Adolescent Psychiatry ◽  
Classification System ◽  
Worked Examples ◽  
Child And Adolescent ◽  
Child And Adolescent Psychiatry ◽  
Classification Systems ◽  
Complex Data ◽  
Practical Applications ◽  
Practical Implications

Objective: Classification systems and their practical implications have become increasingly important in child and adolescent psychiatry. This paper presents the evolution and practical applications of a multiaxial classification system for children and adolescents presenting to mental health services. Included are some worked examples demonstrating both the complexity of many presentations and how broadening the use of the multiaxial system can help in identifying appropriate interventions. Conclusions: Classification systems in child and adolescent psychiatry have largely remained uniaxial in nature. A multiaxial system encapsulates the broader biopsychosocial aspects of the presenting child or adolescent, and orders complex data in a concise manner. This approach can be used to concisely communicate with other treating clinicians, and assist with case reviews, formulation and teaching.

scholarly journals Predicting drag on rough surfaces by transfer learning of empirical correlations

2021 ◽  
Vol 933 ◽  
Author(s):  
Sangseung Lee ◽  
Jiasheng Yang ◽  
Pourya Forooghi ◽  
Alexander Stroh ◽  
Shervin Bagheri
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Rough Surfaces ◽  
High Fidelity ◽  
Empirical Correlations ◽  
Practical Applications ◽  
Learning Framework ◽  
Recent Developments ◽  
Drag Prediction ◽  
Surface Statistics

Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical applications. In this study, we propose a transfer learning framework to model the drag on irregular rough surfaces even with a limited amount of direct numerical simulations. We show that transfer learning of empirical correlations, reported in the literature, can significantly improve the performance of neural networks for drag prediction. This is because empirical correlations include ‘approximate knowledge’ of the drag dependency in high-fidelity physics. The ‘approximate knowledge’ allows neural networks to learn the surface statistics known to affect drag more efficiently. The developed framework can be applied to applications where acquiring a large dataset is difficult but empirical correlations have been reported.

scholarly journals Bearing State Recognition Method Based on Transfer Learning Under Different Working Conditions

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 234
Author(s):  
Ning Cao ◽  
Zhinong Jiang ◽  
Jinji Gao ◽  
Bo Cui
Keyword(s):  
Transfer Learning ◽  
Kernel Function ◽  
Working Conditions ◽  
Rolling Bearing ◽  
Learning Performance ◽  
State Recognition ◽  
Practical Applications ◽  
Balanced Distribution ◽  
The Cross ◽  
Balance Factor

Bearing state recognition, especially under variable working conditions, has the problems of low reusability of monitoring data, low state recognition accuracy and low generalization ability of the model. The feature-based transfer learning method can solve the above problems, but it needs to rely on signal processing knowledge and expert diagnosis experience to obtain the cross-characteristics of different working conditions data in advance. Therefore, this paper proposes an improved balanced distribution adaptation (BDA), named multi-core balanced distribution adaptation (MBDA). This method constructs a weighted mixed kernel function to map different working conditions data to a unified feature space. It does not need to obtain the cross-characteristics of different working conditions data in advance, which simplifies the data processing and meet end-to-end state recognition in practical applications. At the same time, MBDA adopts the A–Distance algorithm to estimate the balance factor of the distribution and the balance factor of the kernel function, which not only effectively reduces the distribution difference between different working conditions data, but also improves efficiency. Further, feature self-learning and rolling bearing state recognition are realized by the stacked autoencoder (SAE) neural network with classification function. The experimental results show that compared with other algorithms, the proposed method effectively improves the transfer learning performance and can accurately identify the bearing state under different working conditions.

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