Exploration and Practice of Talent Cultivation Reform Mode in the Measurement a Control Technology and Instrument Specialty of Local Agricultural Universities

Abstract

Abstract-The talent cultivation mode of innovation and entrepreneurship is the inherent requirement to realize the transformation and development of local universities and improve the quality of applied talent cultivation. Aiming at the problems of the separation of education and production in the agricultural engineering of the local agricultural college, and the lagging of the course content in the development of the industry, as well as combining the characteristics of local economic development, this paper sets up a goal of scientific talent cultivation, builds up a new talent cultivation operation mode and puts into practice accordingly, at the same time, provides reference for local undergraduate innovative talent cultivation.

Classroom Behaviour of Talent Cultivation in Colleges and Universities Supported by Deep Learning Technology

“…In this section, we estimate the usefulness of our preferred strategy in contrast to the previously mentioned methods for Chinese students' data for the given data. Our proposed technique method also matched with other standard techniques artificial intelligence (AI) [26], machine learning (ML) [27], particle swarm optimization-support vector machine (PSO-SVM) [28], and Fuzzy k nearest neighbor (F-KNN) [29]. Several significant disadvantages of artificial intelligence applications, according to AI, are the safety risk associated with submitting critical material to virtualized systems that may require proper security controls [30].…”

“…The proposed method has enhanced accuracy due to the results obtained thus far. Existing works in AI [26], ML [27], PSO-SVM [28], and F-KNN [29] are compared using measures such as precision, F1 score, student performance, and accuracy. As a result, compared to existing works, the proposed CNN-AM performs better.…”

“…The proposed novel convolution neural network with attention mechanism and statistical analysis approaches are functional according to this section. The performance metrics like precision, F1 score, student performance, and accuracy are used to compare the existing works like artificial intelligence (AI) [26], machine learning (ML) [27], particle swarm optimization-support vector machine (PSO-SVM) [28], and Fuzzy k nearest neighbor (F-KNN) [29].…”