scholarly journals Research on Defect Recognition of Lithium Battery Pole Piece Based on Deep Learning

2021 ◽  
Vol 261 ◽  
pp. 01021
Author(s):  
Jiwei Li ◽  
Linsheng Li ◽  
Changlu Xu
Keyword(s):  
Deep Learning ◽  
Network Model ◽  
Lithium Battery ◽  
High Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Learning Network ◽  
Defect Recognition ◽  
Deep Learning Network

In the field of defect recognition, deep learning technology has the advantages of strong generalization and high accuracy compared with mainstream machine learning technology. This paper proposes a deep learning network model, which first processes the self-made 3, 600 data sets, and then sends them to the built convolutional neural network model for training. The final result can effectively identify the three defects of lithium battery pole pieces. The accuracy rate is 92%. Compared with the structure of the AlexNet model, the model proposed in this paper has higher accuracy.

scholarly journals Malware Classification Using Machine Learning and Image Processing

2022 ◽  
Vol 10 (1) ◽  
pp. 531-541
Author(s):  
Zainab Mushtaq
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computer Security ◽  
Transfer Learning ◽  
Learning Technology ◽  
Test Dataset ◽  
Learning Network ◽  
Malware Classification ◽  
Learning Technique ◽  
Deep Learning Network

Abstract: Malware is routinely used for illegal reasons, and new malware variants are discovered every day. Computer vision in computer security is one of the most significant disciplines of research today, and it has witnessed tremendous growth in the preceding decade due to its efficacy. We employed research in machine-learning and deep-learning technology such as Logistic Regression, ANN, CNN, transfer learning on CNN, and LSTM to arrive at our conclusions. We have published analysis-based results from a range of categorization models in the literature. InceptionV3 was trained using a transfer learning technique, which yielded reasonable results when compared with other methods such as LSTM. On the test dataset, the transferring learning technique was about 98.76 percent accurate, while on the train dataset, it was around 99.6 percent accurate. Keywords: Malware, illegal activity, Deep learning, Network Security,

Recognition Method of Abnormal Behavior of Marine Fish Swarm Based on In-Depth Learning Network Model

2021 ◽  
Author(s):  
Liyong Chen ◽  
Xiuye Yin
Keyword(s):  
Deep Learning ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Model ◽  
Marine Fish ◽  
Recognition Accuracy ◽  
Abnormal Behavior ◽  
Fish School ◽  
Learning Network ◽  
Deep Learning Network

In order to solve the problem that individual coordinates are easily ignored in the localization of abnormal behavior of marine fish, resulting in low recognition accuracy, execution efficiency and high false alarm rate, this paper proposes a method of fish abnormal behavior recognition based on deep learning network model. Firstly, the shadow of the fish behavior data is removed, and the background image is subtracted from each frame image to get the gray image of the fish school. Then, the label watershed algorithm is used to identify the fish, and the coordinates of different individuals in the fish swarm are obtained. Combined with the experimental size constraints and the number of fish, and combined with the deep learning network model, the weak link of video tag monitoring of abnormal behavior of marine fish is analyzed. Finally, the multi instance learning method and dual flow network model are used to identify the anomaly of marine fish school. The experimental results show that the method has high recognition accuracy, low false alarm rate and high execution efficiency. This method can provide a practical reference for the related research in this field.

scholarly journals Recognition of corona virus disease (COVID-19) using deep learning network

2021 ◽  
Vol 11 (1) ◽  
pp. 365
Author(s):  
Ashwan A. Abdulmunem ◽  
Zinah Abdulridha Abutiheen ◽  
Hiba J. Aleqabie
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Virus Disease ◽  
Learning Networks ◽  
Accuracy Rate ◽  
X Ray ◽  
Infected People ◽  
Learning Network ◽  
Corona Virus ◽  
Deep Learning Network

Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.

scholarly journals Interfruit : Deep Learning Network for Classifying Fruit Images

2020 ◽  
Author(s):  
Wenzhong Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Accuracy ◽  
Fruit Color ◽  
The Self ◽  
Test Set ◽  
Broad Application ◽  
Learning Network ◽  
Direct Processing ◽  
Deep Learning Network

AbstractFruit classification is conductive to improving the self-checkout and packaging systems. The convolutional neural networks automatically extract features through the direct processing of original images, which has attracted extensive attention from researchers in fruit classification. However, due to the similarity of fruit color, it is difficult to recognize at a higher accuracy. In the present study, a deep learning network, Interfruit, was built to classify various types of fruit images. A fruit dataset involving 40 categories was also constructed to train the network model and to assess its performance. According to the evaluation results, the overall accuracy of Interfruit reached 93.17% in the test set, which was superior to that of several advanced methods. According to the findings, the classification system, Interfruit, recognizes fruits with high accuracy, which has a broad application prospect.

scholarly journals Deep Learning Data Privacy Protection Based on Homomorphic Encryption in AIoT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yichuan Wang ◽  
Xiaolong Liang ◽  
Xinhong Hei ◽  
Wenjiang Ji ◽  
Lei Zhu
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Privacy Protection ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Accuracy Rate ◽  
Learning Network ◽  
Shared Data ◽  
Data Privacy Protection ◽  
Deep Learning Network

With the rapid development of 5G technology, its high bandwidth, high reliability, low delay, and large connection characteristics have opened up a broader application field of IoT. Moreover, AIoT (Artificial Intelligence Internet of Things) has become the new development direction of IoT. Through deep learning of real-time data provided by the Internet of Things, AI can judge user habits more accurately, make devices behave in line with user expectations, and become more intelligent, thus improving product user experience. However, in the process, there is a lot of data interaction between the edge and the cloud. Given that the shared data contain a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure multiparty computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-10 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted Cifar-10 can reach 91.35%, slightly lower than the test result in plaintext and better than the existing deep learning network model that can realize data privacy protection.

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