Underwater image restoration via backscatter pixel prior and color compensation

Advances in network technology have led to extensive information technology construction work in all walks of life; universities, as a key component of national development, cannot be overlooked in this regard. In today’s universities, the Web-based integrated academic management information system is widely used, promoting higher education management system innovation and improving the management level of education departments and teaching management. The traditional management mode is incapable of locating “knowledge” in the mountains of student transcripts, and the original management mode must be improved. In business, finance, insurance, marketing, and other fields, digital exploration technology is widely used. This article describes the design approach for a data mining-based analysis and management system for PE course teaching quality, as well as the application of information technology and data mining technology in PE by combining actual PE teaching in schools, with the goal of realizing a data mining-based PE performance management system to serve PE teaching in schools and improve PE teaching quality. The results show that the time required to find frequent itemsets using a traditional algorithm running on a single machine, as well as the time required to scan the database several times for frequent itemset search in a distributed cluster of 20 computing nodes, is significantly longer than that required by the data mining algorithm. As a result, the proposed sports performance management system is functional, simple, and scalable, with each functional module operating independently and cooperatively, reflecting the concept of “high cohesion and low coupling.”

Section: Design Ideas Of Teaching Quality Analysis and Management Sys...mentioning

confidence: 99%

Design of Teaching Quality Analysis and Management System for PE Courses Based on Data-Mining Algorithm

Luo

2022

“…In the field of civil aviation emergency management, Hong W proposed a few-shot learning method. Finally, experiments show that the method can solve the problem of automatic updating of concepts and relationships in large-scale domain ontologies while also providing good data support, and using few-shot machine learning [ 9 – 11 ] to train general neural network models in cell lines also has many advantages for high-throughput screening of individual patients. To summarize, many scholars have studied the few-shot learning method and application in depth after several years of research.…”

Section: Introductionmentioning

confidence: 99%

Online and Offline Interaction Model of International Chinese Education Based on Few-Shot Learning

Cao

Cai

2022

Few-shot learning is a method to acquire learning ability in a small amount of sample data scenarios. This paper aims to study an online and offline interaction model applied to international Chinese education and teaching based on the few-shot learning method. It first expounds an overview of international Chinese education, including the connotation and characteristics of the hybrid mode of online teaching and offline teaching in international Chinese education, then designs an online and offline interactive model, and finally compares it with a baseline model and a traditional single teaching model. The experimental data shows that the accuracy and recall rate of the international Chinese education online and offline interaction model based on few-shot learning both reach 70%, which verifies its effectiveness.

“…Image processing [ 14 , 15 ], zoom-in and zoom-out, random cropping, adding noise, mirror flipping, and other basic data augmentation methods based on generative models are among the most basic. These manual transformers can create new images that have the same tags as the old ones.…”

Section: Few-shot Learning Based On Contrastive Average Lossmentioning

confidence: 99%

Dance Fitness Action Recognition Method Based on Contour Image Spatial Frequency Domain Features and Few-Shot Learning

Chi

2022

In recent years, the research work of artificial intelligence technology has progressed rapidly, and various classic Few-Shot learning models have achieved unprecedented success in many artificial intelligence application fields. These include face recognition, object classification detection and tracking, speech recognition, and natural language processing, which greatly facilitate our lives. This paper aims to identify dance fitness movements based on contour image spatial frequency domain features and Few-Shot learning technology. This paper proposes a Few-Shot learning method based on contrastive average loss for Few-Shot learning. This method makes the learned model more representative by improving the loss function and performing a normalization process, and it proposes a feature extraction algorithm that combines improved LBP and HOG for action recognition technology. The experimental results show that the recognition accuracy of the algorithm in this paper is 93.10%, 90.30%, and 92.70% for walking, opening hands, and running, respectively. This illustrates the effectiveness of the fusion feature algorithm.