Research on Artificial Intelligence Machine Learning Character Recognition Algorithm Based on Feature Fusion

He, Dongdong; Zhang, Yaping

doi:10.1088/1742-6596/2136/1/012060

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…Zhang believed that the Asian stock market has a monthly effect. e monthly effect means that in a particular month of the year, the average stock return is higher or lower than the rest of the year [8].…”

Section: Introductionmentioning

confidence: 99%

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Lee

Huang

Tang

2022

Security and Communication Networks

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With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence has developed comprehensively in different fields, and it has been widely used in the field of the financial economy. Machine learning under artificial intelligence is currently widely used in stock market volatility dynamics and related research. This paper applied machine learning to the prediction of the dynamic relationship of Asian stock market volatility and established a model for predicting the dynamic relationship of stock market volatility under machine learning. By using statistical theory, linear support vector machines, generalizable bounds, and other algorithms, it provides the theoretical basis and feasibility analysis for the model. Through investigation and research, this paper found that compared with ordinary forecasting model methods, the stock volatility dynamic trend forecasting model based on machine learning has a relatively complete forecasting effect, and the accuracy of the machine learning forecasting model was up to 52%. The lowest was 39%, the average prediction accuracy was 46.5%, and the accuracy was improved by 16.8%. This showed that the introduction of machine learning prediction models in the dynamic prediction model of Asian stock volatility is relatively successful.

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Section: Introductionmentioning

confidence: 99%

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Lee

Huang

Tang

2022

Security and Communication Networks

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“…Finally, 80 images of esophageal cancer cells were selected for the experiment and annotated using the Labelme tool. To improve the generalization ability of the model and reduce the phenomenon of over-fitting [4] , data enhancement is performed on the images, and finally, 240 images of 256×256 are obtained. Among them, 220 images are used for training, and 20 images are used for testing.…”

Section: Datasetmentioning

confidence: 99%

Research on improved Unet in microscopic image segmentation of esophageal cancer

et al. 2022

J. Phys.: Conf. Ser.

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Timely diagnosis can greatly reduce the mortality of esophageal cancer. However, the existing diagnostic methods can only rely on pathologists to observe and estimate through stained images of naked eye slices because of lacking advanced analysis methods. These methods will cause a long testing cycle and will have different degrees of error in the results of testing. Therefore, we put forward a cell segmentation model based on the basic framework of Unet network and introduced an attention mechanism to the model. First, the Unet with encoder-decoder structure is used for initial segmentation based on esophageal cancer cell images. Then, the attention mechanism is used to heavy the weight of the cell region to reduce the interference of the model caused by the low contrast between esophageal cancer living cells under an optical microscope image and the background. As a result, the recognition accuracy of the improved model for esophageal cancer cells can reach 96%. The experimental results show that the improved model reaches an accuracy of 96% in recognizing esophageal cancer cells. The proposed model can provide a real-time, objective, and accurate solution to the diagnosis of esophageal cancer.

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Handwritten Character Evaluation and Recommendation System

Mittal

Bhatia

Garg

2023

Lecture Notes in Electrical Engineering

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Research on Artificial Intelligence Machine Learning Character Recognition Algorithm Based on Feature Fusion

Cited by 5 publications

References 3 publications

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning

Research on improved Unet in microscopic image segmentation of esophageal cancer

Handwritten Character Evaluation and Recommendation System

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