Synthesis, topological structure and magnetism of a scarce homochiral octanuclear metallarectangle

Ding, De‐Gang; Liu, Zhijing; Zhang, Heli; Xu, Jianxing; Hou, Xuehui; Wu, Jing-Yuan

doi:10.1016/j.ica.2021.120281

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Neural network is an algorithm model for dealing with complex information [8]. Based on topology [9], process input data. In the process of data transmission, the gradient is constantly used to adjust the error between the actual output and the expected output of parameters to achieve minimum convergence, and the adaptability is the best, which greatly improves the robustness of data processing.…”

Section: Deep Feedforward Neural Networkmentioning

confidence: 99%

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Cheng

2022

Wireless Communications and Mobile Computing

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This paper takes the data of a university graduate as the research object. By consulting various literatures and literature analyses, we can understand the impact of students’ academic performance, English level, and other activities on students’ employment development and select data on this basis. The collected data is cleaned, integrated, and transformed to form a standard data set. The factors affecting graduates’ employment are complex and diverse, with high data feature dimensions, sparse links between features, complex and diverse attributes, and both discrete and continuous features. According to the characteristics of college students’ employment data, this paper uses the deep-seated neural network with strong learning ability and adaptability to predict college students’ employment, so as to provide guidance for college students’ employment. Firstly, based on deep learning and Feedforward Neural Network technology, a prediction model of college students’ employment destination with six influencing factors is established, and the prediction accuracy of the model is evaluated. The ACC value and loss value are used as indicators to test whether the prediction effect of the prediction model is good. The results show that the prediction effect of the model is worthy of further research and optimization. Finally, combined with the actual data of graduates, the practical application of the prediction model is analyzed. Compared with the traditional machine learning algorithm, the effectiveness of the algorithm is verified.

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Section: Deep Feedforward Neural Networkmentioning

confidence: 99%

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Cheng

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Synthesis, topological structure and magnetism of a scarce homochiral octanuclear metallarectangle

Cited by 1 publication

References 14 publications

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

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