# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 2 Related Work  
When establishing evaluation models, researchers usually use AHP and TOPSIS in various quantitative evaluations. For example, [PERSON] et al. (2018) evaluated the need to generate flood risk maps for different stakeholders at all levels of administration in the context of the Istanbul floods. Their results showed that various stakeholders are required to participate in the production and modification of the generated flood risk map due to significantly different perspectives. [PERSON] et al. (2018) made a potential risk assessment of self-driving vehicles. Performances of their proposed method were also compared with ordinary fuzzy sets, and it was found that their proposed method produces reliable and informative results, better presenting the decision-making imprecision problem. [PERSON] et al. (2018) explored sustainable suppliers for Iran's steel industry at all levels. Their results showed that Iran's mining industry is the best sustainable supplier of Iran's steel industry. The fields of these researchers above are different; however, they are all close to reality, reflecting the practicability of AHP and TOPSIS. A variety of feasible alternatives and conflicting conditions are to be considered to establish evaluation models. From the related work, they can be summarized as follows:  
* Determining the types of indicators and research objects and collecting relevant data. * Adopting AHP to construct multilevel indicators, which covers risk factors that need to be considered as extensively as possible. * Applying TOPSIS to comprehensively qualify these indicators. * Making sensitivity analyses or case applications to verify model rationality. In establishing a prediction model, deep learning networks such as convolutional neural networks (CNNs), deep neural networks (DNNs) and recurrent neural networks (RNNs) are widely used. For example, [PERSON] et al. [13] proposed a new ensemble CNN architecture based on CNS for the effective detection of malware. The results showed that their model is flexible, practical, and efficient. High detection accuracy and low false-alarm rate are achievable through the original inputs of malware. In addition, [PERSON] et al. [14] collected news content from online social networks, extracted sentiment information from the news and applied it to a new hybrid model called RNN-boost to predict the volatility of China's stock market. Experimental results show that their model is superior to other common methods and achieves good prediction performance. Moreover, [PERSON] et al. [15] proposed an architecture that helps infer emotion expression in text-to-speech (TTS) based on DNN. The subjective evaluation results showed that their proposed model can convey emotional information to a certain extent. However, these neural network models still have disadvantages. In DNN and CNN, the input and output of training samples are relatively certain. However, it is difficult to directly split them into independent samples for training through DNN/CNN when a training sample input is a continuous sequence with a different length, such as a time-based sequence: a segment of continuous speech or a segment of continuous handwritten text. For this type of problem, RNN handles it better. However, RNN also has its own problems, such as gradient explosion or gradient disappearance in practical applications, where RNN, therefore, generally cannot be directly used. As an improved RNN model, LSTM can process sequences of different lengths better. LSTM has been widely used in text generation, machine translation, speech recognition, the generation of image description, mechanical fault diagnosis and prediction, etc. In the field of speech recognition and machine translation, [PERSON] et al. [16] proposed a new method based on attention-guided 3D CNN and an LSTM model for speech-based emotion recognition. In comparison with other methods, their proposed method yields more accurate results. In addition, Google Translation used a seven- to eight-layer LSTM model in 2016.