# Research on Improving the Executive Ability of University Administrators Based on Deep Learning

Authors: Chengyan Wei; Shenxiang Wang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Over the years, experts have focused their research on ways to increase the executive capacity of university administrators. This is because only by improving the quality of execution of college and university administrative personnel can they actively execute various policies and measures, fully exploit their subjective initiative, and ensure the educational reform of colleges and universities. Increasing the executive capacity of administrative staff can help colleges and universities manage more effectively. Therefore, in the development process of higher education institutions, it is necessary to strengthen the execution of administrative staff, especially the need to adhere to the problem as the basic orientation. Take scientific and practical steps to strengthen administrative personnel’s executive ability in light of current issues with administrative management personnel’s executive power, and establish the groundwork for ensuring the quality of management work. Combining deep learning, this paper proposes a path to improve the executive power of college administrators based on deep learning. To begin, familiarize yourself with the deep noise reduction autoencoder model and support vector regression (SVR) theory and build the DDAE-SVR deep neural network (DNN) model. Then, input a small-scale feature index sample data set and a large-scale short-term traffic flow data set for experiments; then, assess the model’s parameters to achieve the optimal model. Finally, use performance indicators such as MSE and MAPE to compare with other shallow models to verify the effectiveness and advantages of the DDAE-SVR DNN model in the execution improvement path output of university administrators and large-scale data sets.


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## Body

## 1. Introduction

In the process of Chinese higher education transition from elite to popular, comprehensive universities have generally achieved leapfrog development under the guidance of national education policies and relying on their own comprehensive advantages. However, as higher education develops, many colleges and universities face challenges in their development. For example, there is a lack of experience in management systems and operating mechanisms, a tendency to seek completeness in the setting of disciplines, a tendency to climb higher in the level of running a school, and a tendency to be greedy in the scale of running a school [1]. The magnificent blueprint can only be achieved by execution, and execution is an indispensable link between goals and results. However, at the management level of many colleges and universities, usually because of the unscientific formulation of the school’s own development plan, the executive power of the administrative staff is weakened, and the overall executive culture of the school is lacking, which ultimately leads to the lack of management, ineffective execution, and low efficiency. At present, in the context of the great development of higher education, how to continuously respond to challenges and opportunities, improve the executive ability of university administrators, and establish an executive organization that meets the characteristics of high-quality comprehensive university organizations and changes in the external environment are topics that need to be explored. Currently, universities generally have their own development plans. How to put these development strategies into practice is inseparable from the super-high executive power of university administrators. Therefore, studying the executive ability of university administrators is very necessary for the university to maintain continuous, healthy, and leap-forward development.
(1)
The construction of executive power is the fundamental to achieve the school’s strategic goals. Gao [2] analyzed the significance of executive power in school management in his book “School Strategic Management,” from the point of view that executive power is a means to overcome various uncertain factors, and executive power is a touchstone to test the quality of school personnel and organization. Proceed from three aspects. Tang [3] believes that excellent execution is a weapon to eliminate loopholes in school management. Because execution is not only a powerful weapon for turning educational planning into reality, it is also a powerful weapon for filling management loopholes, and it is also a powerful weapon for optimizing educational planning programs. Therefore, while the school is scientifically formulating its own development plan, it should place a greater emphasis on enhancing plan execution and assuring the fulfillment of varied plans(2)
The establishment of executive power is essential for the school’s long-term success. The effective application of school policies by administrators, according to Wang [4], is a critical aspect in school development. Only proper implementation may enable strategic goals to be realized, and organizations rely on implementation to function effectively. When a school decides to implement an effective goal, the first consideration is whether the organization can accomplish this goal. The establishment of a dynamic execution organization is the prerequisite for accomplishing the university’s goals(3)
Execution building is a requirement to promote the establishment of a modern university system. Jiang Qingzhe pointed out that, to a certain extent, school execution is related to the survival and development of the school. It plays an important role in advancing the establishment of a modern university system, taking the road of high-level university construction with Chinese characteristics, and realizing the leap-forward development of the school [5]. Based on the above background, this article relies on the rapid development of deep learning technology, uses DNN to analyze the characteristics of administrative personnel, and outputs a personalized path to improve the executive power of university administrative personnel. Unlike traditional machine learning algorithms, deep learning is not a specific algorithm, but a collective term for a series of algorithms that adopt deep learning ideasThe structure of this article is organized as follows. The literary works related to this study are presented in Section2. The proposed methods is explained in Section 3. The experimentation and evaluation of the suggested method are presented in Section 4. Finally, section 5 summarizes the paper’s main points.


## 2. Related Work

Raman et al. [6] called the gap between strategy and actual results the missing link and named it execution. Reference [7] also adopted this expression, defining execution as the missing link between the goal and the result, and gave a note that this statement comes from Darwin’s theory of biological evolution. Reference [8] believes that execution means transforming a strategy into an action plan and measuring the results. From this perspective, execution can be understood as the link from strategy to result, and execution is the ability to transform strategic planning into actual performance. Discussions on the formulation and implementation of strategic planning have been uninterrupted in the past few decades. People’s knowledge and understanding of strategy implementation is far less clear than strategy formulation, and the research results on strategy implementation are far less than those on strategy formulation [9]. The reason is that the premise of strategic research is that as long as the correct strategy is input to the enterprise, the expected result will naturally be produced. The enterprise’s conduct calls this notion into question [10]. According to statistics, 87.5 percent of businesses that have not yet achieved their strategic goals have a clear strategic setting, but only 36.9% have developed a clear strategic execution path; for businesses that have fully achieved their strategic goals, these proportions are 96.5 percent and 81 percent, respectively. The results of a 1999 study by Fortune magazine are similar. The conclusion is that about 70.1% of CEOs fail not because of poor corporate strategy but because the corporate strategy is not effectively implemented [11]. According to Lazebnik et al., execution is the foundation of strategy, a critical organizational component of strategy, a collection of systematic procedures, and a systematic method of exposing reality and acting in accordance with reality [12, 13]. Strategy and execution are processes, not just events, but also continuous processes that are seamlessly intertwined [14]. After more than 20 years of research, according to Nutt, 50% of all decisions made in an organization fail. The main reason was that managers did not implement and implement it seriously [15]. The application of executive power theory to the field of university education management is a new attempt, but there is still a lack of research in this area at home and abroad, and there is no systematic and comprehensive work on the construction of university executive power. When collecting relevant materials, the author can only refer to and cite some related works and recent academic papers on the executive ability of university administrators. The construction of executive power in colleges and universities is a complex system engineering, which not only needs to be studied from the operational level but also needs the support of theoretical research. The current national competition is becoming increasingly fierce, and the competition between culture and education is the key to national competition, which is related to the future development of the country. Therefore, universities all over the world are comprehensively improving their own school-running level and social influence, and our country is also constantly innovating teaching concepts. Under the new wave of education reform, my country’s college education has entered a new stage, and it has become the consensus of many colleges and universities to cultivate all-round talents. The administrative personnel play a protective role in the training of talents and can provide favorable conditions for the development of education.Deep learning is the general term for deep neural network, and it is the result of continuous in-depth research and development of artificial neural network (ANN) [16]. When researching the biological nervous system, ANN is created by simulating the biological process of neurons. The ANN is connected to each other through a number of nodes, and the output of each node is connected to the input of some other nodes to form a network system [17]. Over the past decades, through continuous experiments to sum up experience, people have gradually discovered that as the number of hidden layers (HL) increases, the expressive ability of neural network systems tends to improve, so that they can complete more complex classification tasks and approximate more complex mathematical function models. However, with the increase in the number of layers, the difficulty of network training has rapidly become larger. Gradient diffusion often affects the employment of standard BP algorithms for network training, resulting in very sluggish convergence. Since no effective method has been found to solve this problem, the development of ANNs has stagnated for a long time. Until 1981, “Early Research on the Visual Cortex” edited by Hubel and Wiesel won the Nobel Prize in Physiology & Medicine in recognition of their major contributions to information processing in the visual system [18].Inspired by the above research results, in 2006, Professor Hinton and other scholars published papers entitled “Reducing the dimensionality of data with neural networks” and “Deep Belief Networks” in the “Science” magazine, which opened a wave of deep learning research [19]. These two articles mentioned the following points:
(1)
The bottleneck of the traditional neural network that cannot be effectively trained due to the increase of the number of layers can be overcome by the training method of layer-by-layer initialization(2)
A multihidden-layer ANN outperforms a single-hidden-layer ANN. The network has stronger feature learning capabilities, and the features obtained through independent learning can more profoundly reflect the nature of the data, thereby achieving more effective classification and later summarized with the continuous development of deep learning(3)
It is necessary to build a network model with multiple HLs(4)
A large number of training samples need to be prepared in advance for training the network. These four points constitute the essential difference between deep learning and traditional pattern recognition methods. Since 2012, deep learning has once again made historical breakthroughs, mainly including deeper networks (ResNet), enhanced convolution module functions, from classification to detection (R-CNN and Fast R-CNN), generation of confrontation networks, and the addition of new function modules (FCN, STNet, and CNN+RNN/LSTM)When it comes to processing small-scale evaluation data sets, the adaptive BP neural network (BPNN) model offers a number of advantages. However, because it only has one hidden layer, processing capacity, predictive ability, and modeling expression power will be limited when dealing with complicated and high-dimensional large-scale data sets. Therefore, The DDAE-SVR DNN model is proposed in this study to address the challenge of complicated high-dimensional large-scale data sets. There are many HLs in the DDAE-SVR DNN model. The Adam method is used throughout the unsupervised training phase to dynamically alter the learning step length of each parameter during training. After multiple hidden layers, the spatial characteristics of the original data are converted multiple times, with the purpose of obtaining the fundamental qualities of the reconstructed output data with the least amount of inaccuracy. SVR is used as a predictor for supervised prediction, which enables translating complicated nonlinear interactions to high-dimensional spaces (HDS) in order to create equivalent linear relationships in low-dimensional spaces in a similar way. Input the small-scale administrative staff characteristic data set and the large-scale data set for experiments, and compare them with other shallow models, validating the suggested model’s efficacy and benefits in the output of the executive power development route for university administrators and the processing of large-scale data sets.


## 3. Method

### 3.1. The Basic Model of Deep Neural Network

#### 3.1.1. Autoencoder Model

There are a lot of DNN fundamental models. In this section, the autoencoder model is briefly discussed. An autoencoder was originally introduced in 1986. A feature extraction or dimensionality reduction technique is an unsupervised algorithm. Encoding and decoding networks are part of the autoencoder concept, which has three layers of networks. Errors are propagated backwards through the network using the BP method, and the network layers’ weights and thresholds are continually adjusted to reduce error between original input data and the output data (OIDAOD). Transforming input data from a high-dimensional format into a low-dimensional format is one of the initial steps in the autoencoder processing process. Then, using the decoding network and the error function, compute the error between the OIDAOD and reduce the error to complete the decoding network reconstruction of the OID. The output of this encoder is used to approximate the identity function as closely as possible to the input; its construction is shown in Figure1.Figure 1
Autoencoder model structure.Suppose the input feature vector (IFV) isx=x1,x2,⋯xn, converted to feature vector h=h1,h2,⋯hm in the HL, and the output feature vector (OFV) is y=y1,y2,⋯yn; then, the mathematical expressions for mapping the autoencoder from the input layer (IL) to the intermediate HL and the decoder from the intermediate HL to the output layer (OL) are as follows:
(1)h=Cxi=ACWCxi+ti,y=Dhj=ADWDhj+tj,where C and D are the encoding function and the decoding function, respectively. And AC and AD are the activation functions of encoding and decoding, respectively, and they are generally nonlinear functions. WC and WD and ti and tj are the weight matrix and threshold matrix of the network, respectively.The autoencoder generally adopts the gradient descent method to adjust the weights and thresholds between layers. The purpose is to minimize the error between the IFVx and the OFV y to reconstruct the OI. The cost function is generally the mean square error function or the cross-entropy loss function, and the expression is as follows:
(2)lx,y=1n∑i=1nyi−xi2,(3)lx,y=1n∑i=1nxilogyi+1−xilog1−yi.


#### 3.1.2. Support Vector Regression

In 1995, Vapnik introduced the support vector machine (SVM) to the world for the first time. To maximize the isolation edge between positive and negative samples, a classification hyperplane is used as a decision-making surface, with the isolation edge between positive and negative samples being maximized. This method is most commonly used to solve classification, pattern recognition, and regression problems. When it comes to dealing with difficult nonlinear issues and pattern recognition in vast dimensions, support vector machines provide a number of advantages. It is a rough approximation of structural risk reduction with the goal of achieving good generalization for a small number of learning models in a short length of time. There are several characteristic indicators for the growth of executive power in university administrators, and a complex nonlinear relationship exists between the characteristic indicators and the development results, which makes it difficult to depict numerically. But because of its superior ability to fit nonlinear functions, SVR may be used to address this problem. SVR is used to anticipate the output outcomes of the deep neural network model’s OL, as a result of which this chapter is structured as follows. It is possible to classify SVR as either linear or nonlinear depending on whether it is encased in a HDS or not. A significant portion of this section is devoted to nonlinear regression utilizing SVR because of the intricacy of the nonlinear problem of strengthening the executive capacity of higher education administrators. When used in SVR, the goal of nonlinear regression is to map a difficult nonlinear link to a HDS and then rebuild the linearized relationship in the HDS that has been defined. Assuming the data set isT=x1,y1,x2,y2,⋯,xn,ynxi∈Sn,yi∈S, first define a nonlinear mapping function for the data set T that cannot be linearly separated in the original space Sn. Transform T to a HDS and ensure that ψT exhibits favorable linear regression properties in the feature space F. As a result, to get a linearized representation of nonlinear issues, execute linear regression in the feature space F first and then in the original space Sn. The expression for creating a nonlinear function given a kernel function Pxi,x=ψxi,ψx is as follows:
(4)fx=∑i=1Tβi−βi∗Pxi,x+t.The frequently used kernel functions are as follows, among whichτ is a parameter.
(1)
Linear function(5)Pxi,x=xZxi(2)
Polynomial function(6)Pxi,x=1+xZxiτ(3)
Radial basis function(7)Pxi,x=exp−x−xi2τ2τisthecorewidth(4)
Sigmoid function(8)Pxi,x=tanhxi,x+τ


### 3.2. DDAE-SVR Deep Neural Network

#### 3.2.1. Basic Model

In this chapter, we introduce the DDAE-SVR DNN model, which can be used to handle challenging nonlinear problems or process large-scale data sets, which are difficult to solve with the current shallow neural network model because of its limited computation and modeling capabilities. SVR is utilized as the prediction OL, while deep denoising automatic encoding (DDAE) is used as the training OL in this study. When applying SVR, a linearized link in the set HDS equivalent to the low-dimensional space can be achieved. To denoise the original input data set before doing unsupervised layer-by-layer learning and training to reduce the error between the OI data set and training output data, an autoencoder is used to produce a feature vector from it. As a final OL prediction, SVR should be used, with the characteristics of your original data set being used as input for the SVR algorithm. The following two processes are the most significant in the DDAE-SVR DNN model.(1)
Unsupervised layer-by-layer trainingBefore the original input data is input to the DDAE-SVR DNN model training, because there will be some noise in the original input data that cannot be cleaned, the characteristics of the original input data will be set to 0 according to a certain ratio. To increase the model’s durability and generalizability, noise reduction processing was used. The unsupervised training process is mainly to use the deep noise reduction autoencoder for training. The noise-reduction processed data enters the first DAE from the input layer for training, and the obtained output data is used as the IFV of the second DAE. After training all DAEs by analogy, each is equivalent to a hidden layer of DDAE. The error calculation between the last DAE decoded output data and the OI data is performed; then, the Adam method is used to optimize the error in order to reduce it until the desired accuracy is attained or the number of iterations is achieved.(2)
Supervised fine-tuning processThe feature vector of the last hidden layer of the deep noise reduction autoencoder is used as the IFV of the final OL of the DDAE-SVR DNN model, and the final OL is based on the supervised algorithm SVR as predictor. In this supervised prediction process, the relevant parameters of SVR are tuned to improve the prediction accuracy and efficiency of the entire model.


#### 3.2.2. Feature Index Samples and Data Sets

The neural network model’s deep structure has numerous hidden layers that can extract the properties of the original sample data with high computational capacity. Calculations and modeling may be performed successfully by neural network models that deal with complicated nonlinear issues or the deep structure of large-scale data sets. Therefore, the DDAE-SVR DNN model proposed in this chapter is used to solve the path to improve the executive power of university administrators. Given that traditional university administrators’ personalized indicators are mostly trained and improved from first-level indicators such as understanding, publicity, and implementation, the gender, age, and other aspects of their personalities are rarely improved in order to assess the effectiveness of the model proposed in this chapter. Taking into account the situation of transformation, the training improvement lacks comprehensiveness. In addition to the traditional evaluation indicators, this article adds two first-level indicators of gender and age to construct the evaluation indicator system of this article to comprehensively propose the promotion path of administrative personnel. The new two first-level indicators have a total of 5 second-level indicators, and all input characteristic indicators are shown in Table1.Table 1
Executive ability index of university administrative staff.
First-level indexIndex numberSecondary indexGenderX1ManX2WomanAgeX3≤35X435<A≤50X5>50UnderstandingX6Able to fully understand the superiors’ instructionsX7Basic understanding of superiors’ instructionsX8Unable to understand the superiors’ instructionsPublicityX9Relevant plans or programs are understood by more than 90% of the execution targetsX10Relevant plans or programs are understood by more than 60% of the execution targetsX11Relevant plans or programs are understood by less than 60% of the execution targetsExecutionX12The plan or program can be perfectly implementedX13Occasionally flaws in plans or programX14Frequent flaws in plans or programCreativityX15Frequent creative proposalsX16Occasional creative proposalsX17No creative proposalsWith a total of 17 assessment indexes, this article’s evaluation index system combines the classic evaluation index with the newly introduced evaluation index. Obtain the relevant data set from 2015 to 2020 from the administrative staff management system of a university, the data set formatX1,X2,⋯,X17,y, a total of 1020 sample data. Among these, 17 secondary index data are employed as model input values. Due to the necessity of identifying the model’s projected output value, a specific score segment corresponding to a path of execution power is utilized as the model’s target expected output value, based on the many assessments and training records of the supervision group’s teachers. The normalized preprocessing of the finally obtained data sample is to improve the computational efficiency of the deep neural network model. The sample data is normalized as shown in Table 2.Table 2
Normalized feature index sample data set.
IndexX1X2X3X4X5X6X7X8X9Score0.80.70.60.70.80.80.70.60.8IndexX10X11X12X13X14X15X16X17yScore0.70.60.80.70.60.80.70.63.7-4.8Using the normalized evaluation index sample data set in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively, and set the total number of samples to n. Assuming that the weight matrix of each layer of the deep noise reduction autoencoder model is W, the threshold matrix is t, and ψ represents the deep noise reduction autoencoder model. When the input data set passes ψ, its expression is as follows:When utilizing the normalized evaluation index sample data mentioned in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively. The total number of samples is set to n using the normalized evaluation index sample data. If W indicates the weight matrix of each layer in the deep noise reduction autoencoder model, t denotes the threshold matrix, and ψ denotes the deep noise reduction autoencoder model, then the following is true. When the input data set satisfies the ψ test, the following equation is true:
(9)λ=ψWX+t.Theλ in formulas ((3))–((5)) represents the deep noise reduction autoencoder model’s OFV, λ is utilized as an input to the SVR model for evaluation and prediction, and f is used as the SVR model’s function; the equation is as follows:
(10)ys=fλ.The evaluation sample data set is partitioned into training and test data sets, with the training data set being used to train the model discussed in this chapter. By modifying the number of HLs in the model, the optimization technique, and other parameters, as well as the SVR parameters that affect prediction effectiveness, a stable and ideal model is created. Then, using the test data set, determine if the model is successful at increasing the executive authority of university administrators.


## 3.1. The Basic Model of Deep Neural Network

### 3.1.1. Autoencoder Model

There are a lot of DNN fundamental models. In this section, the autoencoder model is briefly discussed. An autoencoder was originally introduced in 1986. A feature extraction or dimensionality reduction technique is an unsupervised algorithm. Encoding and decoding networks are part of the autoencoder concept, which has three layers of networks. Errors are propagated backwards through the network using the BP method, and the network layers’ weights and thresholds are continually adjusted to reduce error between original input data and the output data (OIDAOD). Transforming input data from a high-dimensional format into a low-dimensional format is one of the initial steps in the autoencoder processing process. Then, using the decoding network and the error function, compute the error between the OIDAOD and reduce the error to complete the decoding network reconstruction of the OID. The output of this encoder is used to approximate the identity function as closely as possible to the input; its construction is shown in Figure1.Figure 1
Autoencoder model structure.Suppose the input feature vector (IFV) isx=x1,x2,⋯xn, converted to feature vector h=h1,h2,⋯hm in the HL, and the output feature vector (OFV) is y=y1,y2,⋯yn; then, the mathematical expressions for mapping the autoencoder from the input layer (IL) to the intermediate HL and the decoder from the intermediate HL to the output layer (OL) are as follows:
(1)h=Cxi=ACWCxi+ti,y=Dhj=ADWDhj+tj,where C and D are the encoding function and the decoding function, respectively. And AC and AD are the activation functions of encoding and decoding, respectively, and they are generally nonlinear functions. WC and WD and ti and tj are the weight matrix and threshold matrix of the network, respectively.The autoencoder generally adopts the gradient descent method to adjust the weights and thresholds between layers. The purpose is to minimize the error between the IFVx and the OFV y to reconstruct the OI. The cost function is generally the mean square error function or the cross-entropy loss function, and the expression is as follows:
(2)lx,y=1n∑i=1nyi−xi2,(3)lx,y=1n∑i=1nxilogyi+1−xilog1−yi.


### 3.1.2. Support Vector Regression

In 1995, Vapnik introduced the support vector machine (SVM) to the world for the first time. To maximize the isolation edge between positive and negative samples, a classification hyperplane is used as a decision-making surface, with the isolation edge between positive and negative samples being maximized. This method is most commonly used to solve classification, pattern recognition, and regression problems. When it comes to dealing with difficult nonlinear issues and pattern recognition in vast dimensions, support vector machines provide a number of advantages. It is a rough approximation of structural risk reduction with the goal of achieving good generalization for a small number of learning models in a short length of time. There are several characteristic indicators for the growth of executive power in university administrators, and a complex nonlinear relationship exists between the characteristic indicators and the development results, which makes it difficult to depict numerically. But because of its superior ability to fit nonlinear functions, SVR may be used to address this problem. SVR is used to anticipate the output outcomes of the deep neural network model’s OL, as a result of which this chapter is structured as follows. It is possible to classify SVR as either linear or nonlinear depending on whether it is encased in a HDS or not. A significant portion of this section is devoted to nonlinear regression utilizing SVR because of the intricacy of the nonlinear problem of strengthening the executive capacity of higher education administrators. When used in SVR, the goal of nonlinear regression is to map a difficult nonlinear link to a HDS and then rebuild the linearized relationship in the HDS that has been defined. Assuming the data set isT=x1,y1,x2,y2,⋯,xn,ynxi∈Sn,yi∈S, first define a nonlinear mapping function for the data set T that cannot be linearly separated in the original space Sn. Transform T to a HDS and ensure that ψT exhibits favorable linear regression properties in the feature space F. As a result, to get a linearized representation of nonlinear issues, execute linear regression in the feature space F first and then in the original space Sn. The expression for creating a nonlinear function given a kernel function Pxi,x=ψxi,ψx is as follows:
(4)fx=∑i=1Tβi−βi∗Pxi,x+t.The frequently used kernel functions are as follows, among whichτ is a parameter.
(1)
Linear function(5)Pxi,x=xZxi(2)
Polynomial function(6)Pxi,x=1+xZxiτ(3)
Radial basis function(7)Pxi,x=exp−x−xi2τ2τisthecorewidth(4)
Sigmoid function(8)Pxi,x=tanhxi,x+τ


## 3.1.1. Autoencoder Model

There are a lot of DNN fundamental models. In this section, the autoencoder model is briefly discussed. An autoencoder was originally introduced in 1986. A feature extraction or dimensionality reduction technique is an unsupervised algorithm. Encoding and decoding networks are part of the autoencoder concept, which has three layers of networks. Errors are propagated backwards through the network using the BP method, and the network layers’ weights and thresholds are continually adjusted to reduce error between original input data and the output data (OIDAOD). Transforming input data from a high-dimensional format into a low-dimensional format is one of the initial steps in the autoencoder processing process. Then, using the decoding network and the error function, compute the error between the OIDAOD and reduce the error to complete the decoding network reconstruction of the OID. The output of this encoder is used to approximate the identity function as closely as possible to the input; its construction is shown in Figure1.Figure 1
Autoencoder model structure.Suppose the input feature vector (IFV) isx=x1,x2,⋯xn, converted to feature vector h=h1,h2,⋯hm in the HL, and the output feature vector (OFV) is y=y1,y2,⋯yn; then, the mathematical expressions for mapping the autoencoder from the input layer (IL) to the intermediate HL and the decoder from the intermediate HL to the output layer (OL) are as follows:
(1)h=Cxi=ACWCxi+ti,y=Dhj=ADWDhj+tj,where C and D are the encoding function and the decoding function, respectively. And AC and AD are the activation functions of encoding and decoding, respectively, and they are generally nonlinear functions. WC and WD and ti and tj are the weight matrix and threshold matrix of the network, respectively.The autoencoder generally adopts the gradient descent method to adjust the weights and thresholds between layers. The purpose is to minimize the error between the IFVx and the OFV y to reconstruct the OI. The cost function is generally the mean square error function or the cross-entropy loss function, and the expression is as follows:
(2)lx,y=1n∑i=1nyi−xi2,(3)lx,y=1n∑i=1nxilogyi+1−xilog1−yi.


## 3.1.2. Support Vector Regression

In 1995, Vapnik introduced the support vector machine (SVM) to the world for the first time. To maximize the isolation edge between positive and negative samples, a classification hyperplane is used as a decision-making surface, with the isolation edge between positive and negative samples being maximized. This method is most commonly used to solve classification, pattern recognition, and regression problems. When it comes to dealing with difficult nonlinear issues and pattern recognition in vast dimensions, support vector machines provide a number of advantages. It is a rough approximation of structural risk reduction with the goal of achieving good generalization for a small number of learning models in a short length of time. There are several characteristic indicators for the growth of executive power in university administrators, and a complex nonlinear relationship exists between the characteristic indicators and the development results, which makes it difficult to depict numerically. But because of its superior ability to fit nonlinear functions, SVR may be used to address this problem. SVR is used to anticipate the output outcomes of the deep neural network model’s OL, as a result of which this chapter is structured as follows. It is possible to classify SVR as either linear or nonlinear depending on whether it is encased in a HDS or not. A significant portion of this section is devoted to nonlinear regression utilizing SVR because of the intricacy of the nonlinear problem of strengthening the executive capacity of higher education administrators. When used in SVR, the goal of nonlinear regression is to map a difficult nonlinear link to a HDS and then rebuild the linearized relationship in the HDS that has been defined. Assuming the data set isT=x1,y1,x2,y2,⋯,xn,ynxi∈Sn,yi∈S, first define a nonlinear mapping function for the data set T that cannot be linearly separated in the original space Sn. Transform T to a HDS and ensure that ψT exhibits favorable linear regression properties in the feature space F. As a result, to get a linearized representation of nonlinear issues, execute linear regression in the feature space F first and then in the original space Sn. The expression for creating a nonlinear function given a kernel function Pxi,x=ψxi,ψx is as follows:
(4)fx=∑i=1Tβi−βi∗Pxi,x+t.The frequently used kernel functions are as follows, among whichτ is a parameter.
(1)
Linear function(5)Pxi,x=xZxi(2)
Polynomial function(6)Pxi,x=1+xZxiτ(3)
Radial basis function(7)Pxi,x=exp−x−xi2τ2τisthecorewidth(4)
Sigmoid function(8)Pxi,x=tanhxi,x+τ


## 3.2. DDAE-SVR Deep Neural Network

### 3.2.1. Basic Model

In this chapter, we introduce the DDAE-SVR DNN model, which can be used to handle challenging nonlinear problems or process large-scale data sets, which are difficult to solve with the current shallow neural network model because of its limited computation and modeling capabilities. SVR is utilized as the prediction OL, while deep denoising automatic encoding (DDAE) is used as the training OL in this study. When applying SVR, a linearized link in the set HDS equivalent to the low-dimensional space can be achieved. To denoise the original input data set before doing unsupervised layer-by-layer learning and training to reduce the error between the OI data set and training output data, an autoencoder is used to produce a feature vector from it. As a final OL prediction, SVR should be used, with the characteristics of your original data set being used as input for the SVR algorithm. The following two processes are the most significant in the DDAE-SVR DNN model.(1)
Unsupervised layer-by-layer trainingBefore the original input data is input to the DDAE-SVR DNN model training, because there will be some noise in the original input data that cannot be cleaned, the characteristics of the original input data will be set to 0 according to a certain ratio. To increase the model’s durability and generalizability, noise reduction processing was used. The unsupervised training process is mainly to use the deep noise reduction autoencoder for training. The noise-reduction processed data enters the first DAE from the input layer for training, and the obtained output data is used as the IFV of the second DAE. After training all DAEs by analogy, each is equivalent to a hidden layer of DDAE. The error calculation between the last DAE decoded output data and the OI data is performed; then, the Adam method is used to optimize the error in order to reduce it until the desired accuracy is attained or the number of iterations is achieved.(2)
Supervised fine-tuning processThe feature vector of the last hidden layer of the deep noise reduction autoencoder is used as the IFV of the final OL of the DDAE-SVR DNN model, and the final OL is based on the supervised algorithm SVR as predictor. In this supervised prediction process, the relevant parameters of SVR are tuned to improve the prediction accuracy and efficiency of the entire model.


### 3.2.2. Feature Index Samples and Data Sets

The neural network model’s deep structure has numerous hidden layers that can extract the properties of the original sample data with high computational capacity. Calculations and modeling may be performed successfully by neural network models that deal with complicated nonlinear issues or the deep structure of large-scale data sets. Therefore, the DDAE-SVR DNN model proposed in this chapter is used to solve the path to improve the executive power of university administrators. Given that traditional university administrators’ personalized indicators are mostly trained and improved from first-level indicators such as understanding, publicity, and implementation, the gender, age, and other aspects of their personalities are rarely improved in order to assess the effectiveness of the model proposed in this chapter. Taking into account the situation of transformation, the training improvement lacks comprehensiveness. In addition to the traditional evaluation indicators, this article adds two first-level indicators of gender and age to construct the evaluation indicator system of this article to comprehensively propose the promotion path of administrative personnel. The new two first-level indicators have a total of 5 second-level indicators, and all input characteristic indicators are shown in Table1.Table 1
Executive ability index of university administrative staff.
First-level indexIndex numberSecondary indexGenderX1ManX2WomanAgeX3≤35X435<A≤50X5>50UnderstandingX6Able to fully understand the superiors’ instructionsX7Basic understanding of superiors’ instructionsX8Unable to understand the superiors’ instructionsPublicityX9Relevant plans or programs are understood by more than 90% of the execution targetsX10Relevant plans or programs are understood by more than 60% of the execution targetsX11Relevant plans or programs are understood by less than 60% of the execution targetsExecutionX12The plan or program can be perfectly implementedX13Occasionally flaws in plans or programX14Frequent flaws in plans or programCreativityX15Frequent creative proposalsX16Occasional creative proposalsX17No creative proposalsWith a total of 17 assessment indexes, this article’s evaluation index system combines the classic evaluation index with the newly introduced evaluation index. Obtain the relevant data set from 2015 to 2020 from the administrative staff management system of a university, the data set formatX1,X2,⋯,X17,y, a total of 1020 sample data. Among these, 17 secondary index data are employed as model input values. Due to the necessity of identifying the model’s projected output value, a specific score segment corresponding to a path of execution power is utilized as the model’s target expected output value, based on the many assessments and training records of the supervision group’s teachers. The normalized preprocessing of the finally obtained data sample is to improve the computational efficiency of the deep neural network model. The sample data is normalized as shown in Table 2.Table 2
Normalized feature index sample data set.
IndexX1X2X3X4X5X6X7X8X9Score0.80.70.60.70.80.80.70.60.8IndexX10X11X12X13X14X15X16X17yScore0.70.60.80.70.60.80.70.63.7-4.8Using the normalized evaluation index sample data set in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively, and set the total number of samples to n. Assuming that the weight matrix of each layer of the deep noise reduction autoencoder model is W, the threshold matrix is t, and ψ represents the deep noise reduction autoencoder model. When the input data set passes ψ, its expression is as follows:When utilizing the normalized evaluation index sample data mentioned in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively. The total number of samples is set to n using the normalized evaluation index sample data. If W indicates the weight matrix of each layer in the deep noise reduction autoencoder model, t denotes the threshold matrix, and ψ denotes the deep noise reduction autoencoder model, then the following is true. When the input data set satisfies the ψ test, the following equation is true:
(9)λ=ψWX+t.Theλ in formulas ((3))–((5)) represents the deep noise reduction autoencoder model’s OFV, λ is utilized as an input to the SVR model for evaluation and prediction, and f is used as the SVR model’s function; the equation is as follows:
(10)ys=fλ.The evaluation sample data set is partitioned into training and test data sets, with the training data set being used to train the model discussed in this chapter. By modifying the number of HLs in the model, the optimization technique, and other parameters, as well as the SVR parameters that affect prediction effectiveness, a stable and ideal model is created. Then, using the test data set, determine if the model is successful at increasing the executive authority of university administrators.


## 3.2.1. Basic Model

In this chapter, we introduce the DDAE-SVR DNN model, which can be used to handle challenging nonlinear problems or process large-scale data sets, which are difficult to solve with the current shallow neural network model because of its limited computation and modeling capabilities. SVR is utilized as the prediction OL, while deep denoising automatic encoding (DDAE) is used as the training OL in this study. When applying SVR, a linearized link in the set HDS equivalent to the low-dimensional space can be achieved. To denoise the original input data set before doing unsupervised layer-by-layer learning and training to reduce the error between the OI data set and training output data, an autoencoder is used to produce a feature vector from it. As a final OL prediction, SVR should be used, with the characteristics of your original data set being used as input for the SVR algorithm. The following two processes are the most significant in the DDAE-SVR DNN model.(1)
Unsupervised layer-by-layer trainingBefore the original input data is input to the DDAE-SVR DNN model training, because there will be some noise in the original input data that cannot be cleaned, the characteristics of the original input data will be set to 0 according to a certain ratio. To increase the model’s durability and generalizability, noise reduction processing was used. The unsupervised training process is mainly to use the deep noise reduction autoencoder for training. The noise-reduction processed data enters the first DAE from the input layer for training, and the obtained output data is used as the IFV of the second DAE. After training all DAEs by analogy, each is equivalent to a hidden layer of DDAE. The error calculation between the last DAE decoded output data and the OI data is performed; then, the Adam method is used to optimize the error in order to reduce it until the desired accuracy is attained or the number of iterations is achieved.(2)
Supervised fine-tuning processThe feature vector of the last hidden layer of the deep noise reduction autoencoder is used as the IFV of the final OL of the DDAE-SVR DNN model, and the final OL is based on the supervised algorithm SVR as predictor. In this supervised prediction process, the relevant parameters of SVR are tuned to improve the prediction accuracy and efficiency of the entire model.


## 3.2.2. Feature Index Samples and Data Sets

The neural network model’s deep structure has numerous hidden layers that can extract the properties of the original sample data with high computational capacity. Calculations and modeling may be performed successfully by neural network models that deal with complicated nonlinear issues or the deep structure of large-scale data sets. Therefore, the DDAE-SVR DNN model proposed in this chapter is used to solve the path to improve the executive power of university administrators. Given that traditional university administrators’ personalized indicators are mostly trained and improved from first-level indicators such as understanding, publicity, and implementation, the gender, age, and other aspects of their personalities are rarely improved in order to assess the effectiveness of the model proposed in this chapter. Taking into account the situation of transformation, the training improvement lacks comprehensiveness. In addition to the traditional evaluation indicators, this article adds two first-level indicators of gender and age to construct the evaluation indicator system of this article to comprehensively propose the promotion path of administrative personnel. The new two first-level indicators have a total of 5 second-level indicators, and all input characteristic indicators are shown in Table1.Table 1
Executive ability index of university administrative staff.
First-level indexIndex numberSecondary indexGenderX1ManX2WomanAgeX3≤35X435<A≤50X5>50UnderstandingX6Able to fully understand the superiors’ instructionsX7Basic understanding of superiors’ instructionsX8Unable to understand the superiors’ instructionsPublicityX9Relevant plans or programs are understood by more than 90% of the execution targetsX10Relevant plans or programs are understood by more than 60% of the execution targetsX11Relevant plans or programs are understood by less than 60% of the execution targetsExecutionX12The plan or program can be perfectly implementedX13Occasionally flaws in plans or programX14Frequent flaws in plans or programCreativityX15Frequent creative proposalsX16Occasional creative proposalsX17No creative proposalsWith a total of 17 assessment indexes, this article’s evaluation index system combines the classic evaluation index with the newly introduced evaluation index. Obtain the relevant data set from 2015 to 2020 from the administrative staff management system of a university, the data set formatX1,X2,⋯,X17,y, a total of 1020 sample data. Among these, 17 secondary index data are employed as model input values. Due to the necessity of identifying the model’s projected output value, a specific score segment corresponding to a path of execution power is utilized as the model’s target expected output value, based on the many assessments and training records of the supervision group’s teachers. The normalized preprocessing of the finally obtained data sample is to improve the computational efficiency of the deep neural network model. The sample data is normalized as shown in Table 2.Table 2
Normalized feature index sample data set.
IndexX1X2X3X4X5X6X7X8X9Score0.80.70.60.70.80.80.70.60.8IndexX10X11X12X13X14X15X16X17yScore0.70.60.80.70.60.80.70.63.7-4.8Using the normalized evaluation index sample data set in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively, and set the total number of samples to n. Assuming that the weight matrix of each layer of the deep noise reduction autoencoder model is W, the threshold matrix is t, and ψ represents the deep noise reduction autoencoder model. When the input data set passes ψ, its expression is as follows:When utilizing the normalized evaluation index sample data mentioned in Table1, the format is X1,X2,⋯,X17,y, where X and y represent feature vector data and target label data, respectively. The total number of samples is set to n using the normalized evaluation index sample data. If W indicates the weight matrix of each layer in the deep noise reduction autoencoder model, t denotes the threshold matrix, and ψ denotes the deep noise reduction autoencoder model, then the following is true. When the input data set satisfies the ψ test, the following equation is true:
(9)λ=ψWX+t.Theλ in formulas ((3))–((5)) represents the deep noise reduction autoencoder model’s OFV, λ is utilized as an input to the SVR model for evaluation and prediction, and f is used as the SVR model’s function; the equation is as follows:
(10)ys=fλ.The evaluation sample data set is partitioned into training and test data sets, with the training data set being used to train the model discussed in this chapter. By modifying the number of HLs in the model, the optimization technique, and other parameters, as well as the SVR parameters that affect prediction effectiveness, a stable and ideal model is created. Then, using the test data set, determine if the model is successful at increasing the executive authority of university administrators.


## 4. Experiment and Analysis

In this chapter, we defined the model analysis and comparison, model parameter analysis, and comparison with shallow model briefly.


### 4.1. Model Analysis and Comparison

The small-scale feature data used in this article uses the data set in Section3.2, while the large-scale data set uses the short-term traffic flow data set. Because large-scale feature data involves a large amount of privacy and is difficult to collect, large-scale short-term traffic flow data is used instead for verification, and the data structures of the two are similar and the dimensions are similar. If you use a deep noise reduction autoencoder, you should set the activation function of the Sigmoid function, the learning rate to 0.001, the accuracy goal to 0.001, the maximum number of training repetitions (5,000), and the weight to be allocated at random, all with a threshold value of 0. The deep noise reduction autoencoder introduces the Adam algorithm and optimizes the number of HL and the number of neurons to minimize the error between the output data and the original data to obtain the essential characteristics of the original data. The important parameters of SVR are adjusted in the supervised OL to improve the prediction accuracy of the model. In order to verify that the DDAE-SVR deep neural network model proposed in this paper has more advantages than other models in terms of the promotion path of university administrators, in this section, the mean absolute percentage error (MAPE), mean square error (MSE), symmetric average absolute percentage error (SMAPE), and root mean square error (RMSE) are introduced as performance comparison indicators of model prediction accuracy. Their formulas are as follows, where n represents the sample size of the test data, Ri represents the actual true value of the test data, and Pi represents the predicted value of the test data by the model.
(11)MAPE=1n∑i=1nRi−PiRi,MSE=1n∑i=1nPi−Ri2,SMAPE=1n∑i=1nPi−RiRi+Pi/2,RMPE=∑i=1nPi−Ri2n.


### 4.2. Model Parameter Analysis

Because a model’s output is the executive power route of university administrators, it is critical to optimize the model’s predictive performance by adjusting the proper parameters. I believe that is just the beginning. Experimentation is a continuous process of adjusting critical parameters in order to increase the model’s calculating abilities and forecast accuracy, and it is essential for this to happen. This section makes use of both unsupervised learning and training as well as supervised prediction output in order to increase the model’s ability to predict the executive power route of university administrators with more reliability.(1)
There are various methods for optimizing the training process of neural network models, such as gradient descent algorithm, RMSProp algorithm, momentum algorithm, and Adam algorithm. While the current gradient descent technique is the most often used optimization strategy in neural network models, the convergence speed is sluggish and it is possible to slip into the local minimum and the gradient vanishes when training neural network models with several HLs. The Adam approach is provided as an optimization technique for the unsupervised learning training of the DDAE-SVR DNN model. First-order and second-order moment estimates are used to dynamically alter the learning step length for each parameter. Stabilizing parameters is a goal for each repeat of the learning process. When using the DDAE-SVR DNN model’s three HLs and 20 neurons in the HL, the mean square error function is employed to calculate the error between the unsupervised training output and the original input data. For training and error estimates, we use the gradient descent technique, RMSProp, the momentum algorithm, and the Adam algorithm. Their error curves are shown in Figure2. Gradient descent and momentum algorithms have been dropping, but their decrease is gradual, and the number of iterations is increasing, resulting in a slow convergence rate, according to the chart. However, error convergence tends to flatten as the number of iterations grows. The first 500 iterations of RMSProp and Adam algorithms show a fast decrease in the OFV and original data errors. The graphic shows that the Adam algorithm is the best in recreating the original input data; hence, it has been selected as an ideal method for the process of unsupervised learningFigure 2
Error comparison of different optimization algorithms.The number of HLs in the DNN model is set to between two and five, with the number of neurons in each HL set to twenty, in order to determine the optimal number of HL for the DNN model while taking into consideration the size of the sample data set. Adam algorithm is a learning and training approach that is used to improve unsupervised learning and training processes. It is also known as Adam algorithm. Feed the properties of the sample data set into the DDAE-SVR DNN model during the training phase. After unsupervised training of the deep noise reduction autoencoder, the error curve between the output data feature vector and the original input data set is displayed in Figure3. Given a constant number of HL, the disparity between the reconstructed output data and the original input data shrinks rapidly as the number of recurrent training sessions grows. With a rise in HL, the error develops gradually as long as the number of repeated training sessions stays constant. Therefore, when the number of HL in the DDAE-SVR DNN model is reduced to two, the error between reconstructed output data and the original input data during unsupervised training is reduced to a bare minimum.Figure 3
Error comparison of different hidden layers.This component picks between 20 and 25 neurons for each HL in the DDAE-SVR DNN model. The Adam method is used to maximize the unsupervised learning and training process in the model, which has two HLs. The deep-noise autoencoder will automatically rebuild the error curve between the output data and the original input data after feeding the training data into the model and modifying the number of HL neurons, as shown in Figure4. As the number of iterations increases, the error between the reconstructed output data and the original input data decreases fast. This can be observed in the picture. Iterative training results in a progressive reduction in the error rate as the number of HL neurons increases. That is why 25 is chosen for each HL of the DDAE-SVR DNN. It is now at its best, in terms of inaccuracy, between reconstructed data and the original data used in the unsupervised training procedure.
(2)
Supervised prediction output processFigure 4
Selection of the number of different neurons in the hidden layer.Through the unsupervised learning training of the DDAE-SVR DNN model, the error between the reconstructed output data of the last HL and the original input data is minimized. Therefore, the feature vector of the last hidden layer neuron can be obtained as the IFV of the model prediction OL. The final prediction OL of the model uses SVR as the prediction period to predict the execution power improvement path. However, the main parameters that affect the performance of SVR are the error penalty coefficient,v, and the kernel function type. The error penalty coefficient is the key to adjust the model complexity and empirical risk, v is to control the number of support vectors and training errors, and its value range is (0.1). The kernel function type determines the distribution of sample data in HDS.The kernel function types of SVR are mainly linear functions, polynomial functions, radial basis functions, and Sigmoid functions. In view of the size of the sample data set, the error penalty coefficient range is set to [1], v=0.4. Obtain the eigenvector input of the last hidden layer in the unsupervised training process as the input eigenvector of the OL of the DDAE-SVR DNN model, and use SVR to predict and output, and then, obtain the error curve between the predicted result value and the true value. It can be seen from the Figure 5 that as the error penalty coefficient increases, no matter which kernel function is used as the kernel function of SVR, the output prediction MAPE error will increase. Because the size of the sample data set is small, when the error penalty coefficient increases, it will cause excessive punishment and increase the MAPE. However, when the polynomial function is used as the kernel function of SVR, the prediction effect is better than the other three functions. Therefore, the error penalty coefficient of the SVR model is selected as 1, and the kernel function is selected as the polynomial function.Figure 5
The selection of kernel function and error penalty coefficient.Verify that the DDAE-SVR DNN model’s prediction output is accurate. The SVR of the prediction OL has an error penalty coefficient of 1, the kernel function is a polynomial function, and the value of the parameterv, which regulates the number of support vectors and the training error, is [0.1,1]. Then, the feature vector of the last hidden layer neuron after the unsupervised training of the model is used as the IFV of SVR. The graph of MAPE and MSE between the predicted result and the true value is shown in Figures 6 and 7. It can be seen from the figure that the curve change trends of the MAPE and MSE values are basically the same, which first decreases and then increases with the increase of v. When v=0.4, the prediction error of the model in this chapter is the smallest and the prediction accuracy is the best.Figure 6
Relationship between parameterv and MAPE.Figure 7
Relationship between parameterv and MSE.


### 4.3. Comparison with Shallow Model

DDAE-SVR DNN model is optimized to validate that the model provided in this chapter has more benefits than other shallow models in forecasting the execution power improvement route of university administrators. To minimize the error between the reconstructed output data and the original data, the Adam algorithm is used for optimization, with the number of HLs of the model and the number of neurons at 2 and 25, respectively. The essence of the original data is retrieved. The error penalty coefficient, the number of control support vectors, and the training error parametery are all adjusted in the supervised prediction process, and the kernel function type is set to 1 and 0.4. The polynomial function optimization expenditure vector regression is the most efficient method for predicting model performance. To compare the DDAE-SVR DNN with three shallow models of standard BPNN, SVM, and adaptive BPNN, input the small-scale sample data set established in this article to train and verify the model, and predict the results using antinormalization processing. Table 3 shows the comparative findings for performance measures such as MAPE and MSE, which are utilized as comparison indicators. The table shows that all of the model’s performance measures are optimum when compared to the typical BP neural network model. Although the model presented in this chapter requires more time to train than support vector machines, the other four performance characteristics are superior. However, when compared to the adaptive BPNN, this chapter’s model performs better than it in other performance indicators, especially the two critical indicators of MAPE and MSE. Comparing these two models in this chapter shows that they each have their own benefits and weaknesses when dealing with small-scale data sets, which supports our claim that the neural network model in this chapter is successful.Table 3
Comparison of results of different models.
IndexMAPEMSESMAPERMSETime (s)ModelBPNN0.08548.2502.12%6.85015.211SVM0.06724.6001.65%4.9600.256Adaptive BPNN0.06520.5201.58%4.8800.336DDAE-SVR0.0618.8601.59%4.8005.285Although the DDAE-SVR DNN model has advantages in dealing with small-scale and low-dimensional data on the issue of improving the executive ability of university administrators, it is difficult to reflect the application advantages of this model on large-scale high-dimensional data sets. Therefore, large-scale short-term traffic flow data is selected for experimental verification. Due to the increase in the size of the data set, the previously tuned parameters are all used for small-scale data sets. Therefore, the training process fine-tunes the parameters of the two models again and uses the training time of the model and performance indicators such as MAPE and MSE as comparison indicators. The comparison results of the two models are shown in Table4.Table 4
Performance comparison of large-scale data sets.
IndexMAPEMSESMAPERMSETime (s)ModelAdaptive BPNN0.12663.213.27%7.2434.63DDAE-SVR0.03522.561.36%3.5291.57When compared to an adaptive BPNN, which has a much shorter training period, the models discussed in this chapter are significantly more effective at correcting errors than the adaptive BPNN model. The number of HLs and the number of neurons in the model presented in this chapter is bigger than the adaptive BPNN model, resulting in a considerable amount of computation. There is a very little amount of error in this performance index, which shows that the model presented in this chapter has strong prediction accuracy and convergence to some degree, as well as its powerful computation and modeling abilities.


## 4.1. Model Analysis and Comparison

The small-scale feature data used in this article uses the data set in Section3.2, while the large-scale data set uses the short-term traffic flow data set. Because large-scale feature data involves a large amount of privacy and is difficult to collect, large-scale short-term traffic flow data is used instead for verification, and the data structures of the two are similar and the dimensions are similar. If you use a deep noise reduction autoencoder, you should set the activation function of the Sigmoid function, the learning rate to 0.001, the accuracy goal to 0.001, the maximum number of training repetitions (5,000), and the weight to be allocated at random, all with a threshold value of 0. The deep noise reduction autoencoder introduces the Adam algorithm and optimizes the number of HL and the number of neurons to minimize the error between the output data and the original data to obtain the essential characteristics of the original data. The important parameters of SVR are adjusted in the supervised OL to improve the prediction accuracy of the model. In order to verify that the DDAE-SVR deep neural network model proposed in this paper has more advantages than other models in terms of the promotion path of university administrators, in this section, the mean absolute percentage error (MAPE), mean square error (MSE), symmetric average absolute percentage error (SMAPE), and root mean square error (RMSE) are introduced as performance comparison indicators of model prediction accuracy. Their formulas are as follows, where n represents the sample size of the test data, Ri represents the actual true value of the test data, and Pi represents the predicted value of the test data by the model.
(11)MAPE=1n∑i=1nRi−PiRi,MSE=1n∑i=1nPi−Ri2,SMAPE=1n∑i=1nPi−RiRi+Pi/2,RMPE=∑i=1nPi−Ri2n.


## 4.2. Model Parameter Analysis

Because a model’s output is the executive power route of university administrators, it is critical to optimize the model’s predictive performance by adjusting the proper parameters. I believe that is just the beginning. Experimentation is a continuous process of adjusting critical parameters in order to increase the model’s calculating abilities and forecast accuracy, and it is essential for this to happen. This section makes use of both unsupervised learning and training as well as supervised prediction output in order to increase the model’s ability to predict the executive power route of university administrators with more reliability.(1)
There are various methods for optimizing the training process of neural network models, such as gradient descent algorithm, RMSProp algorithm, momentum algorithm, and Adam algorithm. While the current gradient descent technique is the most often used optimization strategy in neural network models, the convergence speed is sluggish and it is possible to slip into the local minimum and the gradient vanishes when training neural network models with several HLs. The Adam approach is provided as an optimization technique for the unsupervised learning training of the DDAE-SVR DNN model. First-order and second-order moment estimates are used to dynamically alter the learning step length for each parameter. Stabilizing parameters is a goal for each repeat of the learning process. When using the DDAE-SVR DNN model’s three HLs and 20 neurons in the HL, the mean square error function is employed to calculate the error between the unsupervised training output and the original input data. For training and error estimates, we use the gradient descent technique, RMSProp, the momentum algorithm, and the Adam algorithm. Their error curves are shown in Figure2. Gradient descent and momentum algorithms have been dropping, but their decrease is gradual, and the number of iterations is increasing, resulting in a slow convergence rate, according to the chart. However, error convergence tends to flatten as the number of iterations grows. The first 500 iterations of RMSProp and Adam algorithms show a fast decrease in the OFV and original data errors. The graphic shows that the Adam algorithm is the best in recreating the original input data; hence, it has been selected as an ideal method for the process of unsupervised learningFigure 2
Error comparison of different optimization algorithms.The number of HLs in the DNN model is set to between two and five, with the number of neurons in each HL set to twenty, in order to determine the optimal number of HL for the DNN model while taking into consideration the size of the sample data set. Adam algorithm is a learning and training approach that is used to improve unsupervised learning and training processes. It is also known as Adam algorithm. Feed the properties of the sample data set into the DDAE-SVR DNN model during the training phase. After unsupervised training of the deep noise reduction autoencoder, the error curve between the output data feature vector and the original input data set is displayed in Figure3. Given a constant number of HL, the disparity between the reconstructed output data and the original input data shrinks rapidly as the number of recurrent training sessions grows. With a rise in HL, the error develops gradually as long as the number of repeated training sessions stays constant. Therefore, when the number of HL in the DDAE-SVR DNN model is reduced to two, the error between reconstructed output data and the original input data during unsupervised training is reduced to a bare minimum.Figure 3
Error comparison of different hidden layers.This component picks between 20 and 25 neurons for each HL in the DDAE-SVR DNN model. The Adam method is used to maximize the unsupervised learning and training process in the model, which has two HLs. The deep-noise autoencoder will automatically rebuild the error curve between the output data and the original input data after feeding the training data into the model and modifying the number of HL neurons, as shown in Figure4. As the number of iterations increases, the error between the reconstructed output data and the original input data decreases fast. This can be observed in the picture. Iterative training results in a progressive reduction in the error rate as the number of HL neurons increases. That is why 25 is chosen for each HL of the DDAE-SVR DNN. It is now at its best, in terms of inaccuracy, between reconstructed data and the original data used in the unsupervised training procedure.
(2)
Supervised prediction output processFigure 4
Selection of the number of different neurons in the hidden layer.Through the unsupervised learning training of the DDAE-SVR DNN model, the error between the reconstructed output data of the last HL and the original input data is minimized. Therefore, the feature vector of the last hidden layer neuron can be obtained as the IFV of the model prediction OL. The final prediction OL of the model uses SVR as the prediction period to predict the execution power improvement path. However, the main parameters that affect the performance of SVR are the error penalty coefficient,v, and the kernel function type. The error penalty coefficient is the key to adjust the model complexity and empirical risk, v is to control the number of support vectors and training errors, and its value range is (0.1). The kernel function type determines the distribution of sample data in HDS.The kernel function types of SVR are mainly linear functions, polynomial functions, radial basis functions, and Sigmoid functions. In view of the size of the sample data set, the error penalty coefficient range is set to [1], v=0.4. Obtain the eigenvector input of the last hidden layer in the unsupervised training process as the input eigenvector of the OL of the DDAE-SVR DNN model, and use SVR to predict and output, and then, obtain the error curve between the predicted result value and the true value. It can be seen from the Figure 5 that as the error penalty coefficient increases, no matter which kernel function is used as the kernel function of SVR, the output prediction MAPE error will increase. Because the size of the sample data set is small, when the error penalty coefficient increases, it will cause excessive punishment and increase the MAPE. However, when the polynomial function is used as the kernel function of SVR, the prediction effect is better than the other three functions. Therefore, the error penalty coefficient of the SVR model is selected as 1, and the kernel function is selected as the polynomial function.Figure 5
The selection of kernel function and error penalty coefficient.Verify that the DDAE-SVR DNN model’s prediction output is accurate. The SVR of the prediction OL has an error penalty coefficient of 1, the kernel function is a polynomial function, and the value of the parameterv, which regulates the number of support vectors and the training error, is [0.1,1]. Then, the feature vector of the last hidden layer neuron after the unsupervised training of the model is used as the IFV of SVR. The graph of MAPE and MSE between the predicted result and the true value is shown in Figures 6 and 7. It can be seen from the figure that the curve change trends of the MAPE and MSE values are basically the same, which first decreases and then increases with the increase of v. When v=0.4, the prediction error of the model in this chapter is the smallest and the prediction accuracy is the best.Figure 6
Relationship between parameterv and MAPE.Figure 7
Relationship between parameterv and MSE.


## 4.3. Comparison with Shallow Model

DDAE-SVR DNN model is optimized to validate that the model provided in this chapter has more benefits than other shallow models in forecasting the execution power improvement route of university administrators. To minimize the error between the reconstructed output data and the original data, the Adam algorithm is used for optimization, with the number of HLs of the model and the number of neurons at 2 and 25, respectively. The essence of the original data is retrieved. The error penalty coefficient, the number of control support vectors, and the training error parametery are all adjusted in the supervised prediction process, and the kernel function type is set to 1 and 0.4. The polynomial function optimization expenditure vector regression is the most efficient method for predicting model performance. To compare the DDAE-SVR DNN with three shallow models of standard BPNN, SVM, and adaptive BPNN, input the small-scale sample data set established in this article to train and verify the model, and predict the results using antinormalization processing. Table 3 shows the comparative findings for performance measures such as MAPE and MSE, which are utilized as comparison indicators. The table shows that all of the model’s performance measures are optimum when compared to the typical BP neural network model. Although the model presented in this chapter requires more time to train than support vector machines, the other four performance characteristics are superior. However, when compared to the adaptive BPNN, this chapter’s model performs better than it in other performance indicators, especially the two critical indicators of MAPE and MSE. Comparing these two models in this chapter shows that they each have their own benefits and weaknesses when dealing with small-scale data sets, which supports our claim that the neural network model in this chapter is successful.Table 3
Comparison of results of different models.
IndexMAPEMSESMAPERMSETime (s)ModelBPNN0.08548.2502.12%6.85015.211SVM0.06724.6001.65%4.9600.256Adaptive BPNN0.06520.5201.58%4.8800.336DDAE-SVR0.0618.8601.59%4.8005.285Although the DDAE-SVR DNN model has advantages in dealing with small-scale and low-dimensional data on the issue of improving the executive ability of university administrators, it is difficult to reflect the application advantages of this model on large-scale high-dimensional data sets. Therefore, large-scale short-term traffic flow data is selected for experimental verification. Due to the increase in the size of the data set, the previously tuned parameters are all used for small-scale data sets. Therefore, the training process fine-tunes the parameters of the two models again and uses the training time of the model and performance indicators such as MAPE and MSE as comparison indicators. The comparison results of the two models are shown in Table4.Table 4
Performance comparison of large-scale data sets.
IndexMAPEMSESMAPERMSETime (s)ModelAdaptive BPNN0.12663.213.27%7.2434.63DDAE-SVR0.03522.561.36%3.5291.57When compared to an adaptive BPNN, which has a much shorter training period, the models discussed in this chapter are significantly more effective at correcting errors than the adaptive BPNN model. The number of HLs and the number of neurons in the model presented in this chapter is bigger than the adaptive BPNN model, resulting in a considerable amount of computation. There is a very little amount of error in this performance index, which shows that the model presented in this chapter has strong prediction accuracy and convergence to some degree, as well as its powerful computation and modeling abilities.


## 5. Conclusion

Under the new situation, the competition among the comprehensive education levels among universities is becoming increasingly fierce. It is not only necessary to continuously innovate teaching methods for the teaching staff, but also to improve the executive ability and master more political theories. The more the ability to assess and solve problems in complicated settings, the better the results in serving instructors and students and adopting higher-level policies will be. As a result, university administrators should understand and apply the spirit of university ideological and political work conferences, improve implementation, and contribute to the cause of higher education. A strategy for the promotion path of university administrators based on deep neural networks is provided in this research, which also introduces the deep noise reduction autoencoder and SVR theory. The deep noise reduction autoencoder is used for unsupervised training, and SVR is used for supervised prediction to construct the DDAE-SVR DNN model. Then, the model is used to output the promotion path of university administrators. Next, optimize and analyze the parameters of the unsupervised training process and the supervised output process of the model in this chapter. One purpose is to minimize the error between the reconstructed output data and the original input data to obtain the essential characteristics of the original data, and the other purpose is to improve the nonlinear processing capability of SVR to improve the prediction accuracy of supervised prediction. Finally, input small-scale feature data sets and large-scale short-term traffic flow data sets into the model for training and verification, and use the model’s training time and error performance indicators such as MAPE and MSE to compare with other shallow models and adaptive BP neural networks. In terms of small-scale evaluation data sets, although the training time of the model in this chapter is higher than that of the adaptive BP neural network model, other error indicators are slightly better than it. Although the number of hidden layers and neurons in the model in this chapter is higher than that of the adaptive BP neural network model, which causes a large amount of calculation and a long training time, the model in this chapter’s other error performance indicators is far better than the adaptive BP neural network model, which verifies the effectiveness of the model in this chapter in terms of the path output of large-scale data sets.



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*Source: 6354801-2022-06-09.xml*