In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the characteristics and rules of teachers' control of classroom emotion nowadays using big data, find the parts of classroom emotion, and design a classroom emotion recognition model based on convolutional neural network speech emotion algorithm according to these characteristics. This paper will investigate the factors and patterns of teachers' emotional control in the classroom. In this paper, the existing neural network is adapted and improved, and some preprocessing is performed on the current dataset to train the network. The network used in this paper is a combination of convolutional neural network (CNN) and recurrent neural network (RNN), which takes advantage of both CNN for feature extraction and RNN for memory capability in the sequence model. This network has a good effect on both object labeling and speech recognition. For the problem of extracting emotion features of whole-sentence speech, we propose an attention mechanism-based emotion recognition algorithm for variable-length speech and design a spatiotemporal attention module for the speech emotion algorithm and a convolutional channel attention module for the CNN network to reduce the contribution of the spatiotemporal data of the speech emotion algorithm and the unimportant parts of the CNN convolutional channel feature data in the subsequent recognition by the attention mechanism. In turn, the weight of core key data and features is increased to improve the model recognition accuracy. As CNN is applied in the speech domain and shows excellent performance, researchers have started to study the effectiveness of CNN in learning emotional features in speech signals, and many CNN-RNN-based frameworks for speech emotion recognition have been proposed. However, many emotion recognition problems are still waiting to be solved. We still do not know what kind of network is the most suitable and what feature extraction method can get the most compelling feature representation . The accThe human brain has a compelling information processing capability, which can perceive and understand the part of the information in a speech where the audio reflects the speaker's emotion; therefore, they can sense the change in the speaker's sentiment in address. As the most direct and effective way for people to communicate and transmit information daily, speech contains textual and acoustic data . AuditorEmotions are the attitudes and experiences of human beings after comparing objective things with their own needs, which are closely related to human production life and have a vital role in human decision-making, interaction, and cognitive process . For theEnough research has been done on classroom emotions to analyze the behavior of teachers and students in the classroom, their emotional expressions, and the effect on the school after these expressions. As early as the 1960s, psychologist DE Caigny A began to join in studying categories related to the emotional goals of teaching and learning and proposed an effective system of classifying emotional domains and goals. The foundational value of education is to achieve development rather than growth than a challenge . The claMore and more researchers have been conducting in-depth research in speech emotion recognition. As the study progresses, many open-source tools have emerged, the most popular of which is the genuine smile and opener toolkits developed by Ocquaye et al. This toolkit extracts speech features, providing excellent information convenience for many researchers . These tWith the application of deep learning in SER, scholars have increasingly demanded the recognition performance of the model and proposed many deep learning-based SER algorithms . Unlike Speech emotion recognition algorithms can mainly include machine learning algorithms based on KNN, random forest, SVM, and methods based on deep learning techniques DNN, CNN, and RNN. K nearest neighbor algorithm is a simple classification method based on statistics, widely used in classification algorithms . The cort = {, , ⋯, } and the samples to be tested x, the class of the models is calculated, where xi ∈ x are the instance's feature vector, the example's feature vector, and the standard type. According to the given distance criterion, the distance between the sample to be measured and all models in the training set is calculated d, I = 1, 2, ⋯, n. And find the location of K nearest points to the piece to be measured, denoted as the set nK(x). Euclidean distance is the most used metric for measuring distance. The formula can express an example:Given the training dataset x is determined in the set nK(x) according to some classification difference rule; in the case of the majority table decision rule, the final category y can be expressed as an equation. The final category of the sample to be tested K value size is an essential factor affecting the performance of the KNN algorithm. In machine learning, cross-validation is often used to select the parameters. The dataset is divided into training, validation, and test sets. The model parameters are decided by observing the performance of the model trained on the training set on the validation set. Finally, the final model version is tested on the test set. For example, overfitting problems will quickly occur if the K value is too small. The K value can be adjusted by observing the model's performance on the validation set to ensure that the model performs optimally on this dataset.In the measure of distance, the choice of Y given a random variable X. Unlike the decision trees of ID3 and C4.5, the decision trees created by ID3 and C4.5 can be multinomial, with the fork under each node determined by the type of node feature, so that the node can be divided into three divisions. The decision tree is assumed to be a bifurcated tree with internal node features taking the values of “yes” and “no.” The left branch supports the value “yes,” and the left supports the value “no.” Such a decision tree is equivalent to recursively bifurcating each feature, dividing the input space into a finite number of cells, and predicting the probability distribution over these cells, i.e., outputting the conditional probability distribution given the input conditions. Random forest improves the model's generalization by randomizing the selection of samples and randomizing the selection of division attributes. Firstly, a random sample with put-back on the dataset by bootstrap is used as a training subset of each decision tree, and multiple subsets are used to train the decision trees separately. Then, during the decision trees training, a certain number of randomly selected features are used as the division attributes of the decision trees to build the decision trees. Finally, all the generated decision trees are voted to produce the final classification results.In machine learning, the performance of a single model is often not very satisfactory. It is often combined with multiple weak classifiers through an integrated learning approach to eventually form a classifier with excellent performance. Random forest is a typical algorithm in blended learning. The weak classifier used in the random forest is the decision tree. The decision tree model is a tree structure where each internal node represents an attribute or feature on which to make a judgment output, and each node represents a classification result. The decision tree divides subsets by selecting features through specific rule algorithms until the samples in the subsets are classified into the same class. The commonly used algorithms are ID3, C4.5, and CART algorithms. CART is a conditional probability distribution that outputs a random variable s = {, , ⋯, } is the feature vectors of the instances, and i = 1, 2 ⋯ , nn is the number of training samples. The core idea of support vector machines is to find a hyperplane that correctly divides the training dataset and requires that this separated hyperplane's geometric interval be maximized. Suppose that for a two-dimensional linear separable space, the dataset M × N, the size of the convolutional kernel W is I × J, the bias is b, the activation function is f, and the output is the speech feature matrix y with dimension size M × N, the convolutional operation of CNN is as follows:Convolutional layer: the main task of the convolutional layer is to automatically learn the input speech features through the convolutional kernel, map the learned features, pass them to the next layer, and learn higher-level features from them based on the lower-level features known in the previous layer. Assuming that the dimension of the two-dimensional input matrix of speech features is Convolutional neural networks have excellent ability in speech emotion feature extraction, image recognition, handwritten Chinese character recognition, etc., through different layer structures, mainly using local connectivity and global sharing, consisting of convolutional layer, pooling, and fully connected layer. In speech recognition, it has good translational and dimensional invariance and is good at handling speech features in two-dimensional space with good robustness and operational efficiency. Its network structure diagram is shown in (2) Pooling layer: the pooling layer, also known as the downsampling layer, has the main task of downsampling the speech emotion features learned from the convolutional layer. Since the feature redundancy obtained from the convolutional kernel (kernel) is high, the pooling layer can remove the redundant information or reduce the marginal insensitive intake. Usually, a local window (2 × 2) is defined on the feature matrix extracted from the convolutional layer, and pooling is performed by window sliding, calculated as an equation.(3) Fully connected layer: the fully connected layer is the key algorithm to compute the advanced speech emotion feature sequences extracted from the upper two layers of CNN and classify and recognize them, which can realize the learning and memory of the emotion feature matrix. The fully connected layer consists of many neurons. The neurons are optimized by forwarding and backward propagation algorithms, weights of feature parameters, and bias training to achieve speech emotion feature recognition. Traditional neural network: no theory to point out how many layers (the number of layers has no role). Convolutional networks: more effective feature learning part, deepening the network to be effective(4) Output layer: the output layer is an essential structure for finalizing the classification. After the whole connection is trained by learning, it outputs a string of countsUsually, a convolutional layer contains multiple convolutional kernels (kernel) to extract different feature information in the same two-dimensional matrix of speech features, equivalent to people observing one thing simultaneously . Still, Therefore, it is necessary to use the output layer to calculate the probability of these results, which can get the maximum likelihood of a specific category to complete the work of classification. Due to the shared convolution kernel, it can handle high-dimensional data well. Need to manually select features and train good weights, i.e., get good results in feature classification. This paper uses the softmax layer as the output layer for type, which converts each neuron's numerical calculation results in the fully connected layer into a relative normalization between 0 and 1. It gives the predicted category based on the close probability. The computational formula is as follows. The primary step in performing speech emotion recognition is to address what emotion is and how to describe and represent it. There are two main models to express human feelings: discrete classification and dimensional. The former of the two approaches is, in essence, a classification problem, and the latter is a regression prediction problem.First, the discrete emotion description model defines a limited number of the universal emotion labels from all human emotion states, which are independent of each other and are called basic emotions. Other emotions are obtained from different permutations of the basic emotions. The four basic emotions recognized and widely used are happiness, anger, sadness, and neutrality. The six basic emotions proposed are happiness, anger, sadness, surprise, disgust, and fear. Since it is easy to understand and has broad applicability, it is being used in more and more emotion recognition fields.The dimensional emotion description model, also known as the continuous emotion description model, describes specific emotion attributes as coordinates in a spatial dimension. Each axis corresponds to a particular attribute of emotion . ExistinThe primary interaction between teachers and students in classroom scenarios is in the form of discourse, and judging emotions through the lesson is also one of the most common ways. Therefore, this chapter studies speech emotion recognition using the speech data generated from teacher-student communication in classrooms . At the People can capture each other's emotional changes through speech because the human brain can perceive and understand the information in speech signals. This information can reflect the speaker's emotional state. On the other hand, speech emotion recognition is a computer simulation of the human emotion perception process, which extracts emotion-related features from speech signals and learns the mapping relationship between these features and emotions. The selection of parts is the key to speech processing, and the goodness of features directly affects the accuracy of speech emotion recognition models. Therefore, how to extract compelling speech emotion features is very important. Classroom observation is a teaching research activity that seeks to improve students' learning and promote teachers' professional development by recording, analyzing, and studying the operation of a classroom teaching or some issues. It requires the observer to observe with a clear purpose and with their senses and aids. Listening to a classroom is an activity of careful observation, which is extremely important for understanding and knowledge of the classroom. There are many commonplace problems in the classroom that can be explored and thought about through the conscious observation of the listener. Listening is a meaningful way to improve the quality of teachers and the quality of classroom teaching.Rhythmic features: in linguistics, rhythm refers to the components of nonindependent segments (vowels and consonants) in speech, forming linguistic functions such as intonation, tone, repetition, and rhythm. Rhyme can reflect various speaker characteristics, including the speaker's emotional information. The verse does not affect the words and phrases of the speech content, but it does affect the true expressed meaning. Given different rhythmic structures, the same sentence can convey different meanings . TherefoVoice quality features: voice quality is a subjective evaluation index of people's speech, used to evaluate speech clarity, cleanliness, and recognition. It is found in the listening and speaking experiments that voice quality is often accompanied by gasping, trilling, choking, etc., when the speaker is emotionally excited, so many researchers have applied voice quality features to the field of speech emotion recognition. Emotion-like feature set: emotion-like feature set is a collection of features designed by experts specifically for emotions. The set includes low-level descriptors (LLDs) and high-level statistics functions (HSFs).The model is improved from the classical CNN model Le Net by replacing the 5 × 5 convolution in the original network structure with two small 3 × 3 convolution kernels, reducing the number of parameters, and using the dropout technique to reduce the degree of overfitting after the two top pooling layers and between the two top layers. The sigmoid activation function of the original model is prone to oversaturation, and the output is 0 or 1 when the absolute value of the input is significant. Still, the gradient of the sigmoid function is close to 0 at these two places, which is prone to gradient loss and leads to an unexpected termination of training. When the input signal exceeds 0, the output value equals the output value of 1.8. Because of its partially linear characteristic, ReLU does not oversaturate. At the same time, ReLU only requires a threshold to obtain the activation value and does not require complex exponential operations like sigmoid, thus reducing the computational effort of training. Sigmoid and ReLU functions are as follows:The KNN and SVM models based on traditional acoustic features perform better than the CNN model based on the speech spectrogram in recognition, especially the SVM, recognizing various emotions and the overall performance. The advantage of KNN is that the model is easy to understand, and you can get good performance by not requiring too much tuning. Trying this algorithm is a good benchmark before considering more advanced techniques. SVM is a small dataset with nonlinear rigorous mathematical logic, high accuracy, and good generalization. In terms of individual emotion recognition, the CNN model performs slightly better than KNN in some indicators of emotions, such as fear and sadness. The model's macro-P, macro-R, macro-F1 values, and recognition rate are comparable to the KNN models, which indicates that the CNN method is feasible to a certain extent. Still, the recognition effect and model performance need to be further enhanced.The experimental data for classroom text-based emotion recognition are derived from the classroom emotion recognition dataset. Pretrained language models have pioneered a new paradigm in NLP research. There are two ways to utilize pretrained models; one is feature-based, which is also a common approach in transfer learning, where the word vectors from the pretrained language model are used as features input to the downstream target task; the other is fine-tuning-based, where the pretrained model is trained directly on the pretrained model and fine-tuned for the downstream target task after convergence. The other one is based on fine-tuning, i.e., training directly on the pretrained model and fine-tuning for the downstream target task after convergence without training the network from scratch . Fine-tuAs with the classroom speech emotion recognition experiments, 20% of the classroom text data were randomly selected as the test set for the experiments. The remaining text data were used to train and evaluate the text classification model. The text data used for training was enhanced locally by both near-synonym replacement and noise introduction, just like speech. The evaluation metrics for the classroom text sentiment classification models were also taken as weighted accuracy, unweighted accuracy, and macro-F1 scores for each category. The experimental evaluation method uses the fivefold cross-validation method. Each fold is taken after the model converges, explicitly using the early-stop method with little difference in model training accuracy within 5 generations. Other models were also tested in this part of the experiment to compare the performance of different models on classroom text emotion classification. The pretrained word embedding vector initializes the word representation. It allows the word embedding representation to be fine-tuned and optimized during the training process. Then, a native recurrent neural network model is constructed for classroom text emotion recognition using GRU computational units, text recurrent encoder, or TRE model for short. One of the reasons for choosing the GRU recurrent neural network is that GRU has similar experimental effects compared to LSTM, but it is easier to compute. The second comparison model is the Bert model. The Client-l12 model is replaced with the Bert Chinese pretraining model to investigate the differences between the two pretraining models. The training results of the three models are shown in The details of the two models, Xlnet-l12 and Tre, are explored in comparison across classroom text sentiment classes. The method used is still error analysis using confusion matrices, the normalized confusion matrix for the Xlnet-l12 model on the test set, and the confusion matrix for the TRE model on the test set. The Client-l12 model has a solid ability to distinguish between silence, doubt, and neutral text emotion classes. Client-l12 outperforms the TRE model in all emotion classes except for the three classroom text emotions of anger, quiet, and surprise, especially in the two emotion classes of doubt and satisfaction. Both have a recall rate of more than 10% compared to the TRE model. This shows that the Xlnet-l1 model uses pretraining and ranked attention mechanisms to deliver more power than the native recurrent neural network model in characterizing text or in the feature extraction of text. The TRE model has extremely high classification ability for the silent classroom text emotion class, followed by better recognition of neutral. The recall of the TRE model on the two emotion classes of silence and anger is like that of the Xlnet-l12 model and has the same performance. Because of silent emotions in the classroom, their text expressions are often composed of blank or a few words that do not contain vibrant color; they can achieve high accuracy for silent emotions, whether the Xlnet-l12 model with the solid representational ability or the slightly inferior TRE model. For anger and hesitation, comparing the previous recognition using speech and the current recognition using text, we can find that the recall rate of rage and uncertainty has not been improved in each modality. On the one hand, each model cannot learn enough differentiated features due to the insufficient sample size of anger and hesitation classes; on the other hand, the model itself has defects, such as model prediction with bias. The comparison of the scores of Xlnet-l12 and TRE classroom emotion recognition models is shown in There will be many network models in training the network. We aim to get network models with good generalization performance and high recognition accuracy, so only the best prediction model and the fitted model are recorded in the experiment. Validation accuracy is a crucial metric for training model generalization. The best prediction model appears when the validation accuracy reaches the maximum value when training the network. The model fits the experimental data better and performs better in speech emotion recognition prediction. The validation accuracy of the improved trained model reaches 92.35% on Emo-DB, the German Berlin sentiment database. The validation accuracy is improved by 8% compared to the built model. Meanwhile, the loss function curve becomes smooth with slight fluctuation after 80 training cycles, and the final convergence value is 0.47. The training model accuracy is shown in The accuracy of the CNN-BLSTM-hybrid distributed attention mechanism model in speech emotion recognition is improved by about 1% compared with the CNN-BLSTM multiheaded attention mechanism model, which proves that the proposed hybrid distributed attention model effectively solves the low-rank bottleneck problem of multiheaded attention mechanism and enhances the expression ability of the model; the accuracy of CNN improved BLSTM multiheaded attention mechanism model. The accuracy of the CNN improved BLST. Multiheaded attention mechanism model is enhanced by about 6% compared with the CNN-BLSTM multiheaded attention mechanism model, which proves that the improved BLSTM proposed in this paper extracts the contextual temporal information of the spectral map more comprehensively and enhances the temporal feature vector of speech. The accuracy of the model presented in this paper is higher than that of other models in the ablation experiment, which proves that the two innovations proposed in this paper are effective in speech emotion recognition and can better integrate the advantages of temporal and spatial features of the two models, which verifies the effectiveness of the model in this paper. To show the improvement effect of hybrid distributed attention more intuitively on multiheaded attention mechanism, different head parameters are designed to conduct experiments based on the hybrid neural network + combination distributed attention model and hybrid neural network + multiheaded attention model proposed in this chapter, and WA is used as the evaluation index, and the experimental results are shown in By comparison, the method proposed in this paper has dramatically improved the recognition accuracy in the category of happy emotions. It has been enhanced from 39% to at most 56%. At the same time, the model's accuracy in recognizing the other three categories of emotions has also improved slightly. Although the fully convolutional network model is not as accurate as the model with recurrent structure, it has an unparalleled advantage in complexity and computational speed. In some mobile devices and tiny sensors, where the model complexity and computational effort are high, it is not easy to meet the requirements using a recurrent structure. The entire convolutional network shows its advantages. However, the confusion matrix also indicates that our model has room for improvement. The accuracy of emotion recognition is only around 80%, which can be improved theoretically by the production and preprocessing of the classroom speech emotion dataset and the training results of the neural network. Data is often more important for deep learning than algorithms because the quality and quantity of data fundamentally determine how well the model is trained. Therefore, the production of datasets and the preprocessing of data are significant. Since the dataset in this paper exceeds the current publicly available datasets in both the number of speakers and the naturalness of speech, the model in this paper has some generalization ability and can be applied to more classroom contents.Classroom observation has always been an essential part of the study of education and teaching. Previously, classroom observations were human-based, and people's subjective judgments were used to evaluate the strengths and weaknesses of a class. The data obtained using a convolutional neural network model will be more objective, and the conclusions obtained from extensive data analysis will be more convincing. The convolutional neural network model saves a lot of workforce and time, and we can get the data quickly without watching a whole class to get the research content more easily. This paper proposes an attention mechanism-based algorithm for emotion recognition of variable-length speech, which focuses on essential features by embedding an attention module. This paper presents a spatiotemporal attention module to assign weights to the address spectrogram features. The convolutional channel attention module pays attention to part of the channel features of the CNN and gives the results. The experimental results show that the proposed method in the paper has a significant improvement in recognition accuracy for each emotion classification compared with that before the model improvement, including a 6.7% improvement in WA accuracy and a 9.1% improvement in UA accuracy. We can get macroscopic data patterns and characteristics through extensive data analysis, which is beneficial for us to grasp the big visible picture and discover the features and shortcomings of emotion teaching.By using deep learning to study classroom emotions and training a speech emotion recognition model, we can rapidly recognize and classify classroom emotions for the following research step. A classroom speech emotion dataset is constructed. This dataset can be used as the basis for training and refining the speech emotion recognition algorithm to improve the recognition accuracy of the training model in the future; likewise, the dataset needs to be more. Similarly, the dataset needs to be improved and expanded to improve the accuracy of classroom emotion recognition. Based on the obtained classroom emotion data, this paper provides an analysis method that classifies classrooms according to the proportion of labels and then proposes the characteristics and shortcomings of current classroom emotion teaching according to the number and characteristics of each classroom category.


The purpose of business sentiment analysis is to determine the emotions or attitudes expressed toward the company, products, services, personnel, or events. Text analysis are the simplest and most developed types of sentiment analysis so far. The text-based business sentiment analysis still has some unresolved challenges. For example, the machine learning algorithms are unable to recognize double meanings, jokes and allusions. The regional differences between language and non-native speech structures cannot be explained. To solve this problem, an undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. To improve the accuracy of sentiment classification, this work proposes a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also present a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value. Business sentiment analysis is to capture and track the opinions, emotions or feelings expressed by consumers when they are participating in various business interactions . SentimeText-based sentiment analysis is a hot research topic in both business and artificial intelligence . Text anA common concept of machine learning in sentiment analysis is that the success of sentiment artificial intelligence “training” always depends on the quality of the input data. Larger, better, and cleaner data sets are necessary to avoid the “garbage in, garbage out” situation . HoweverTo improve the accuracy of business sentiment prediction, this work proposed a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using a classical topic extraction tool. An undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment prediction. Compared with state-of-the-art classification algorithms, the proposed CNN-based prediction model of BuSeD achieves the highest F1 scores. We also present a case study to validate the economic application of business sentiment analysis, which indicates that the news-based business sentiment analysis has high economic application value. The main contribution of this paper is as follows:(1) We propose a CNN-based robust news mining-based business sentiment analysis framework. It can enhance the explainability of the regional differences between language and non-native speech structures by using the undirected weighted graph to model topics in sentences.(2) We propose a CNN-based text sentiment analyzing model to improve the sentiment classification accuracy.(3) We conduct comprehensive experiments to prove the efficiency of the proposed BuSeD.The structure of the rest paper is as follows: Section 2 reviews the state-of-the-art works in text mining and business sentiment analysis. Section 3 presents the details of the proposed BuSeD framework. Section 4 introduces the experiment process and analysis the experiment results. Section 5 concludes this work and gives future research directions.Using web crawling techniques and text mining methods, work  exploredBy using a case study research approach, work  analysesIn work , deep leThe above-mentioned methods do not solve the problem that it is not easy to explain the regional differences between language and non-native speech structures. In addition, the sentiment classification accuracy should be further improved. Therefore, in this paper, we propose a new method to enhance the regional differences between language and non-native speech structures, and to improve the classification accuracy.We propose a news mining and sentiment analysis framework, called BuSeD, to analyze news business sentiment, which can help make economic decisions. It contains two main parts. At first, the news is collected by using crawler tools. The news dataset is preprocessed by reducing noises. Topics in each article is extracted by using traditional topic extraction tools. And then a CNN-based text analyzing model is designed to analyze news from sentence level. The workflow of BuSeD is shown in The news is first extracted by using the general-purpose web crawler (GPWC). GPWC is the whole web crawler. The implementation process is as follows. First, get the initial URL. The initial URL address can be manually specified by the user or determined by one or more initial crawling web pages specified by the user. Second, crawl the page according to the initial URL and get a new URL. After obtaining the initial URL address, you need to crawl the web page in the corresponding URL address first, then store the web page in the original database, find a new URL address while crawling the web page, and store the crawled URL address in a URL list for de duplication and judging the crawling process. Third, put the new URL into the URL queue. After obtaining the next new URL address in the second step, the new URL address will be put into the URL queue. Fourth, read the new URL from the URL queue and crawl the web page according to the new URL. At the same time, get the new URL from the new web page and repeat the above crawling process. Fifth, stop crawling when the stop conditions set by the crawler system are met. When writing crawlers, corresponding stop conditions are usually set. If the stop condition is not set, the crawler will crawl until the new URL address cannot be obtained. If the stop condition is set, the crawler will stop crawling when the stop condition is met.Gensim is used to extract news topics. The input of Gensim is original and unstructured digital text (plain text). The semantic structure of the document is automatically found by calculating the statistical cooccurrence mode in the training corpus. These algorithms are unsupervised, which means that there is no need for manual input - just a set of plain text corpus. Once these statistical patterns are found, any plain text  can be expressed concisely by semantic representation. An undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract.Although business news text has the advantages of strong professionalism and low noise, the “noise interference” caused by massive text data will seriously affect the accuracy of the model. Noise interference means that noises in speech signals can disturb the communication connection between people. The reason is noises can cover up the acoustical components to hide information from listeners. The way to reduce the impact is to remove non-technical nouns and some unimportant prepositions in the original news text. In addition, the research collects common stop words for English and some meaningless digital symbols that may appear in news texts. It also introduces filtering processing to increase the impact of emotional words and verbs, which represent the emotions and views of financial stakeholders on the model.We use the method of k-means clustering to filter noises in the news texts. Clustering is to group a data set into multiple groups or clusters. The data in the same cluster are highly similar, while the data in different clusters are quite different. Typical clustering algorithms include k-means (K-mean) and k-medoids (k-center point) algorithms. K-means-based clustering can realize noise cleaning. It treats the data grouped in smaller clusters as noise. It mainly uses the distance-based noise recognition method. Cluster analysis is carried out on the data set based on the distance between two data.A CNN-based text analyzing model is designed to analyze text sentiment from fine-grained sentence levels. The CNN is composed of an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer. The input is a convolution layer with only one feature surface. Its special network structure enables it to capture small feature information. This technique has been widely used in the field of image recognition. The parameter setting of the proposed CNN model is shown in h(x) in Formula 1 to Γk(x) in Formula 2, which can efficiently speed up the convergence.The input (embedding) layer converts the text into a vector and expands the dimension to meet the parameter requirements of the convolutional neural network. Then the convolutional layer and pooling layer are used to obtain local features and get the most important features. The convolutional layer is connected to the upper-layer feature surface through local connection and uses the feature of weight sharing to reduce the complexity of the model. The pooling layer uses max pooling to extract the most important features. During the training process, the Rectified Linear Unit (ReLU) is used as the activation function to transform the linear neural network into a nonlinear neural network. The output result is changed from k(x) is the ReLU activation function to transform h(x) to non-linear form. k represents the kth convolution window, wT is the weight matrix, and b is the bias value.where h(x) represents the output of the linear output layer. ΓThe corresponding output feature surface size after convolution and pooling operations is done by Formula 3.i is the size of the output feature surface. γsz represents the size of each input feature surface. Wsz is the size of the pooling window. The model is trained by adjusting the number of parameters of the convolutional layers so that the size of ϕi is an integer.where ϕThe features trained by the convolutional layer and the pooling layer are used as the input and output classification results of the fully connected layer. That is, according to the probability distribution of sentences in different categories, output sentiment level labels for each sentence. i and bi are the parameters and biases of the fully connected layer corresponding to the output σi, respectively, and n is the total number of output categories.where θTo avoid over-fitting, dropout is used to make the features learned by neurons more robust. In addition, the model also limits the two-normal form size of the weight vector to achieve better classification results. In terms of measuring the prediction effect, this model uses cross entropy as the loss function. We apply CNN to text sentiment analysis, so the CNN model finally outputs sentiment classification results according to four rating scales, which are extremely negative, negative, positive and extremely positive.We crawl more than 10,000 business news in recent five years from the Web. The training set data consists of 10,000 randomly selected finance and economics related news and 500 automatic abstracts of news retrieved with “business” as the keyword. The news from January 2018 to December 2021 were randomly selected according to the same proportion to ensure that the style of the media reports did not affect the training results. Each sentence was manually annotated, and a total of 80,000 sentences were annotated. The test set consists of 3,000 articles, which are formed by excluding 7,000 news articles selected as the training set from the collected 10,000 news articles and adopt the same labeling method as the training set. The evaluation criteria for sentiment analysis results in this paper are Precision, Recall and F1-Score .To accurately analyze the sentiment in news data, other machine learning methods have also been tried in this work. Naive Bayes is a classification method based on Bayes’ theorem and the assumption of independence of feature conditions, and it is also one of the popular and popular classification models. Because NB needs to estimate few parameters, it is not sensitive to missing data, and the algorithm is relatively simple. In theory, the NB model has the smallest error rate compared with other classification methods, so it can be used as one of the candidate models for comparison of results.Logistic Regression (LR) , also knSupport Vector Machine (SVM)  is a comKNN neighbor algorithm, or K-Nearest Neighbor (KNN) classification algorithm . It is oDynamic convolutional neural network (DCNN) . It inteOne dimension CNN (1D-CNN) . It pretMethods of processing text into word vectors include TF-IDF, Out-of-Dict, Word-Frequency, One-Hot, Word-Frequency and Google’s Word2Vec.We first analyze the high-frequency words of 10,000 news articles. After the stop words are removed, the top 20 words in the number of occurrences are shown in To further verify the validity of the sentiment model, a quantitative trading application based on sentiment analysis is analyzed. The application shows the correspondence between the real-time stock price data trends and the subject sentiment classified by BuSeD. Our study shows that when the stock price keeps rising, the sentiment of the financial news corresponding to the market should be positive. When the stock price keeps falling, the sentiment of the news corresponding to the market should be negative. Based on this hypothesis, we will try to verify the validity of the sentiment model BuSeD by the trend change of stock price and the synchronous change of news sentiment.The stock price trend is mainly analyzed and predicted in terms of the stock price trend, news popularity and public opinion analysis at runtime. In terms of stock price trends, the study obtained the daily high/low and opening/closing prices of official stock prices through multiple channels and customized the corresponding stock price curve and box charts. In terms of news popularity, the research counted the news popularity of Hang Seng Index constituent stocks within 8 years and set up real-time follow-up on each day. In terms of public opinion analysis, the opinions, and sentiments of financial stakeholders on the stock price trend are extracted based on the BuSeD model.The research uses the financial public opinion analysis module to analyze the relationship between news sentiment and stock price. Financial public opinion analysis refers to using BuSeD to analyze the sentiment polarity of news, and then infer the state of the market and the future stock price trends based on the direct relationship between the number of positive and negative news and stock price fluctuations. Through analysis, there is a strong correlation between news public opinion and stock price. When the positive news increases sharply or the negative news accumulates sharply, the market is in a turning period, and the stock price also moves on the corresponding direction.To improve the accuracy of sentiment classification, we proposed a robust news mining-based business sentiment analysis framework, called BuSeD. It has two main components: news collection and preprocessing, and feature extraction and sentiment classification. In news collection and preprocessing component, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In feature extraction and sentiment classification component, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conducted comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also presented a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.MY proposed the idea and wrote the manuscript. BJ discussed with MY for proposing the idea and writing the manuscript. YW collected dataset and conducted the experiment. TH helped to collect dataset and provided suggestions in revision. YL helped to coding. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The vibration and impact of a humanoid bipedal robot during movements such as walking, running and jumping may cause potential damage to the robot’s mechanical joints and electrical systems. In this paper, a composite bidirectional vibration isolator based on magnetorheological elastomer (MRE) is designed for the cushioning and damping of a humanoid bipedal robot under foot contact forces. In addition, the vibration isolation performance of the vibration isolator was tested experimentally, and then, a vibration isolator dynamics model was developed. For the bipedal robot foot impact, based on the vibration isolator model, three vibration reduction control algorithms are simulated, and the results show that the vibration damping effect can reach 85%. Finally, the MRE vibration isolator hardware-in-the-loop-simulation experiment platform based on dSPACE has been built to verify the vibration reduction control effect of the fuzzy PID algorithm. The result shows the vibration amplitude attenuates significantly, and this verifies the effectiveness of the fuzzy PID damping control algorithm. Most current humanoid robots consist of rigid structures . AlthougTherefore, the vibration damping of humanoid robots has become a hot research topic for scholars at home and abroad in recent years. This problem has been addressed by passive vibration isolation methods, such as installing cushioned support brackets at important parts  and addiMagnetorheological elastomers are prepared by mixing soft magnetic particles and polymer elastomers, which have the advantages of both liquids and elastomers, and overcoming the drawbacks of magnetorheological liquids, such as easy settling and poor stability. This incredible tunability makes magnetorheological elastomers a favorable application for semi-active vibration control in mechanical systems. Based on the superior stiffness damping controllability of magnetorheological elastomers, domestic and international scholars have made various attempts on magnetorheological elastomer vibration isolators for different practical engineering applications. Fengrong Bi et al. from Tianjin University designed a variable stiffness and damping damper based on MRE, which has obvious variable characteristics . Fu et aThe work proposed a composite bidirectional MRE vibration isolator designed for the vibration and impact problem of the robot after contact with the ground, using a magnetorheological elastomer, an intelligent material, to be installed on the foot of the robot. The stiffness and damping of the vibration isolators were adjusted in real time according to the vibration and shock they were subjected to, improving the humanoid robot’s impact resistance to complex environments. Performance tests, dynamics modelling and semi-active control of the MRE isolator were carried out in this paper. In order to simulate the actual situation of the robot walking, this paper completed a virtual prototype-based dynamics simulation to analyze the vibration and impact on the robot foot. It is adapted to the semi-active vibration isolator designed in this paper to provide a new solution to the robot vibration damping problem.The inverted pendulum model is a common model-based approach to gait planning for bipedal robots . BipedalThe data curves for the contact forces between the robot’s foot and the ground can be calculated through the Adams simulation analysis in According to the vibration situation of the robot’s feet, a vibration isolator based on the magnetorheological elastomer is designed. Given the relationship between the direction of motion of the magnetorheological elastomer in the vibration isolation support and the direction of the applied magnetic field, the two main modes of operation are the shear mode and pull–press mode, the principle of which is shown in In this manuscript, carbonyl iron powder (type: MPS-MRF-15) is chosen as the magnetic particle. Silicone rubber (type: RTV-704) with good adhesion and aging resistance is chosen as the elastic matrix, and dimethyl silicone oil is chosen as the auxiliary additive. The carbonyl iron powder, dimethyl silicone oil and 704 silicone rubber were first mixed in a beaker at a mass ratio of 8:1:1 and dispersed well using a mixer. The mixture was then placed in a vacuum drying oven to extract the air inside from the stirring process in three stages, followed by pouring the defoamed mixture into a mold and placing it in the drying oven again to extract the air three times and, finally, covering the mold with a lid and placing it in a magnetizing device at 800 mT for 2 h to pre-structure the anisotropic MRE material.The force on the foot during robot walking is a multidirectional force, which inevitably leads to vibration and shock in multiple directions. Conventional MRE isolators for one direction only are not a reasonable solution to this problem. This manuscript therefore attempts to design a composite bidirectional MRE isolator, which is capable of isolating both vertical and horizontal directions. The working principle of the designed composite bidirectional MRE isolator is expressed in The bidirectional MRE isolator designed in this paper is a strictly cylindrical axisymmetric structure, so the simulation analysis is carried out by a one-half model in the X-Z plane. The material properties of each part are first defined via Maxwell’s material library, followed by the selection of the appropriate air boundaries as boundary conditions. Then, we define the loading current density and the number of winding turns for the coil. Finally, the division of the mesh and the solution options setting are made, and then, the simulation can be carried out. The magnetic field distribution of the MRE vibration isolator the one-half model with a 2-A current applied is shown in MREs are a type of viscoelastic material, which means both viscous and elastic deformation mechanisms exist. The mechanical properties of viscoelastic materials are shown in effK denotes the equivalent stiffness of the isolator, maxX and minX denote the maximum and minimum displacement values of the MRE isolator and xmaxF and xminF denote the force output of the MRE isolator at the maximum and minimum displacement values, respectively. eqC denotes the equivalent damping of the isolator, S denotes the area of the force–displacement hysteresis loop, f denotes the excitation frequency and X denotes the excitation amplitude.Based on the mechanical properties, the following equations for equivalent stiffness and equivalent damping are deduced :(1)Keff=In order to further establish the mechanical performance of the designed MRE vibration isolator, a performance test rig was set up for testing in both the vertical and horizontal directions. The sinusoidal excitation was used to perform the experiments in order to verify the vibration isolator’ s damping and vibration isolation performance. Simulation experiments for random signals will be carried out in our future research. In addition, both the sinusoidal excitation and the foot–ground contact force are periodic signals. Therefore, experiments with periodic signals are more in line with the scenario of robots. During the test experiments in the vertical direction, the signal generator was to generate different sinusoidal excitations, while the current provided by the DC supply was increased from 0 A to 2 A. The slope of the long axis of the ellipse represents the equivalent stiffness of the isolator, and the area of the ellipse represents the equivalent damping of the isolator as the applied current increases, as shown in In the horizontal direction, aiming to test the performance of the MRE vibration isolator under more working conditions, the signal generator was to generate sinusoidal excitation frequency of 8 Hz, and the displacement amplitudes at each excitation frequency were 0.4 mm, 0.6 mm and 0.8 mm, respectively. The current supplied by the DC supply is from 0 A to 2 A. The 8-Hz sinusoidal excitation experiments are shown in Liu Tao et al. ,28 propoBoth simulation and semi-physical experiments require a dynamic model of the vibration isolator. In this paper, the Kelvin model is selected to describe the mechanical properties of the MRE vibration isolator, which mainly equates the viscoelastic material as a combination of spring and viscous pot units. In conjunction with the foot–ground collision in the previous gait simulation of robot walking, the MRE vibration isolation system is modeled as shown in m0 denotes the unsprung mass, i.e., the part of the robot below the MRE isolator, and m1 denotes the sprung mass, i.e., the sum of the mass of the robot body and the mass of the core in the MRE isolator. k0 and c0 denote the stiffness and damping coefficients set during the collision between the robot’s foot and the ground in the gait simulation, respectively. k1 and c1 denote the initial equivalent stiffness and damping of the vibration isolator without an applied magnetic field. x0(t) denotes the excitation displacement, i.e., the vibration displacement of the robot after vibration and shock, and x0(t) denotes the response displacement, i.e., the vibration displacement transmitted to the robot body through the MRE isolator. F denotes the foot–ground contact force obtained in the previous simulation, and cF denotes the magnetic field control force generated when the isolator is energized, which is obtained by controlling the equivalent stiffness and damping of the MRE isolator.From the dynamics model, the equations of motion for this MRE vibration isolation system can be obtained as follows:The vibration isolator model is known to be in contact with the robot’s foot–ground signal. Simulated vibration isolation experiments are accomplished in Simulink for three algorithms and compared to the vibration isolator output for the no-isolation case.Fuzzy control is robust and well-suited to the control study of MRE vibration isolators. The design flow of the fuzzy control focuses on the design of its controller, which is built in Simulink in a block diagram, as shown in e(t) denotes the deviation, i.e., the difference between the actual output and the theoretical value. pk, ik and dk denote the scale factor, the integral time constant and the differential time constant, respectively. The magnetically induced control force of the MRE isolator cannot meet the control force output by the PID controller at all times, so the control force output by the PID must be limited, as shown in Equation (5):u denotes the control force output from the PID controller, and maxF denotes the maximum magnetically induced control force of the vibration isolator.The PID control law adopted for the vibration isolators in this paper is as follows:e and the deviation change rate de/dt as the input of the fuzzy controller, output pk, ik and dk and reasoning using fuzzy control rules to rectify the three parameters of the PID in real time, and eventually, the PID controller completes the real-time control of the controlled object. The setup of this control algorithm is similar in principle to the first two, where the fuzzy inference rules draw on the practical experience of relevant experts, and the rules for the three parameters are similar. A block diagram is therefore established in Simulink as follows in The fuzzy PID control algorithm adjusts the parameters of the PID in real time through the inference capability of the fuzzy controller. Fuzzy PID control algorithms come in various forms, but their basic operating principle remains the same. The fuzzy PID algorithm uses deviation Based on the foot contact force curve and the MRE vibration isolator dynamics model, a block diagram of the integrated MRE vibration isolation control system was constructed, as shown in The analysis of the results in conjunction with the above leads to the following conclusions:(1) Overall, the MRE vibration isolator seems to have a fabulous damping effect in the robot walking process. The peak acceleration and root mean square (RMS) values of the robot foot vibration signal are reduced by more than 85% under the MRE vibration isolator’s processing. The vibration displacement amplitude is within 4 mm, which means it has a small impact on the robot control accuracy.(2) From the acceleration control effect, comparing several control algorithms, PID control reveals the best control effect on the acceleration peak, followed by fuzzy PID control, while fuzzy control has the third-place effect. The acceleration RMS value is the best effect of fuzzy PID control algorithm, followed by PID control, and the third-place is still fuzzy control.(3) In terms of displacement control, the PID control algorithm is the least effective, the fuzzy control is the second-most effective, and the fuzzy PID has the highest peak displacement. This is because, in buffered damping, in order to cope with the impact of higher acceleration, a certain amount of vibration amplitude is demanded to better offset the impact effect, i.e., by increasing the displacement in exchange for a tinier acceleration. Therefore, fuzzy and PID control of the displacement control is more expressive than fuzzy PID.To investigate the rationality of the control algorithm, a test bench for vibration isolation systems based on dSPACE is built, as shown in The experimental procedure for the whole vibration isolation control system is as follows: Firstly, the control system is set up in a computer using the MATLAB/Simulink R2018a module, as shown in 2 without the control to 0.3539 m/s2 with the fuzzy PID control applied, which decreases by 38.3% and has a better control effect. The effectiveness and reasonableness of the fuzzy PID algorithm were confirmed.This paper presents a composite bidirectional MRE vibration isolator solution for the cushioning and vibration damping of a robot foot: a composite bidirectional MRE vibration isolator is designed using magnetorheological elastomers with high magnetorheological effects. Based on the vibration and impact signals to which the foot is subjected during walking, three controllers: fuzzy, PID and fuzzy PID, are designed, and the damping effects of the control algorithms are compared with a simulation analysis. The experimental platform of the vibration isolation control system is finally built by means of a dSPACE hardware-in-the-loop-simulation system, and the effectiveness of the MRE vibration isolator and the designed control algorithm is verified for sinusoidal excitation signals. The significances and impacts of this work are as follows:Firstly, this manuscript proposes a dynamical approach to the gait analysis of a bipedal robot, providing an important vibration isolation perspective where the forces on the robot’s foot cause vibrations. Secondly, this paper designed a bidirectional vibration isolator using MREs with high magnetorheological effects and conducted experiments to demonstrate its good bidirectional dynamic performance, offering a solution for scenarios requiring multidirectional engineering vibration isolation. Finally, three intelligent control algorithms with good results were designed in this paper, enriching the ideas of intelligent algorithm control.


The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.  The busiest department and most flocked area in the hospital is the emergency room (ED), which serves as the forefront of medical care. Patients come to ED for acute illness and critical conditions. Quick and correct patient evaluation and feedback on medical advice are important. The medical team is required to make treatment decisions under huge pressure and in a short amount of time. Physicians and nurses assigned to ED are usually stressed because they need to attend to patients with diverse medical conditions, which, oftentimes, change rapidly. It is therefore vital to make real-time, personalized, and accurate disease predictions in the ED. Unfortunately, the support of the hospital information system (HIS) for clinical decision-making is still insufficient . AlthougArtificial intelligence (AI) in healthcare is becoming increasingly essential due to its potential to enhance clinical risk predictions and advanced administration within the organization. Thus, implementing AI to achieve these desired benefits is expected in ED . TherefoHowever, past research claims that the current medical healthcare AI available is only used to assist in medical decision-making. Thus, our hospitals adopted a spontaneous use, without compulsion, and the AI predictive results were provided for reference . As a reIn light of the early successful experience in the RCC, we integrated the AI outcome prediction models for the eight types of disease diagnosis in the ED. This was achieved in the form of a dashboard that monitors the prognostic risk of the patients currently in the ED. Therefore, the purpose of this study is to systematically share the successful experience of CMMC in developing this ED AI dashboard, and to serve as an important reference for the development of AI in other hospitals by providing the overall AI infrastructure and software operation mode.Under the instructions of the board of directors and the president, CMMC officially established the Center for Big Medical Data and AI Computing (AI Center) in May 2019 as the base for medical AI development for the three hospitals of the Chi Mei Medical Group (CMMG). The AI Center has three main goals: (1) to establish a medical big data database (data warehouse); (2) to promote AI research; and (3) to develop AI applications. A data scientist and professor hosts the AI Center and recruits data analysis experts and information management experts. In addition, the Department of Information Systems serves as a critical external support for the establishment of AI infrastructure, including the establishment of a big data server, graphical processing unit (GPU) server, and web service (WS) server. After its establishment, CMMG actively encouraged various medical departments in Chi Mei hospitals to develop AI based on big data from EMR . As of June 2022, more than 20 AI models for prognosis prediction have been developed and implemented in clinical practice.®). The big data are then used as a data source for machine learning and other retrospective studies. The design purposes of the database and data warehouse are completely different. The former generally refers to any collection of data organized for storage, accessibility, and retrieval, while the latter is a special database that replicates and integrates the data of the operation process  to facilitate statistics and analysis. Chi Mei builds two levels of big data; one is a general-purpose data warehouse, that is, the data used in most ML research, such as lab data and physicians’ orders; the other is a specific-purpose data warehouse, designed for specific fields of ML studies, such as stroke and cancer. The emerging artificial intelligence of things (AIoT) combines AI and the Internet of things (IoT) and involves the integration of sensing technologies to solve real-world problems [Chi Mei’s AI computing infrastructure  in order to effectively manage multiple users, perform modeling, allocate computing resources, distribute models, and manage versions.Machine learning often incorporates a huge amount of data ; thus, it is necessary to build a GPU server to improve the computing efficiency. In addition, even though there are many free machine learning development platforms , we adopted a professional ML development platform  to form a model bank, which was then placed on a web service platform. As long as the existing HIS sends the necessary feature values  required by a specific AI model to the web service platform through the URL connection, the associated AWS will return the prediction result .The raw data include the existing HIS database and external/open database . After integrating it into the AI web services, the HIS is equipped with AI-assisted functions, which can be called AI-enabled HIS.We used service-oriented architecture (SOA)  to buildHWSThe HWS receives the calls from the existing ED system and returns the prediction results to the ED system. The HWS also provides the interactive functions allowing physicians to adjust feature values to re-predict and simulate the possible changes in the prognosis.FWSThe FWS receives the calls from the HWS to capture the patients’ feature values  which are then returned to the HWS. The FWS can also use IoT technology to extract patients’ bedside physiological information .AWSWe developed all the AI prediction models into AI WSs (AWS) and stored them in the Model Bank. The Model Bank is installed on the AI Web Service Platform to await connection calls from the HIS. The AWS receives the calls of the HWS, obtains the patients’ feature values, and returns the results of the AI prediction  to HWS. For example, the AWS of ED chest pain returns the risk probability of acute myocardial infarction to the HWS.Based on the huge EMR, we successively developed eight prognostic prediction systems (in WS form) in the ED. The steps of the model building and implementation are shown in We developed several outcome prediction models for each type of patient , and each prediction model was built with multiple machine learning and deep learning algorithms . For example, we simultaneously used five algorithms to build the prediction model for death due to influenza in the ED among the elderly . We then compared the quality of the five models built to determine which was the best one. Usually, the one with the highest AUC (the area under the ROC or receiver operator characteristic curve) is the best model, but the balance between sensibility and specificity still needs to be considered.Our feature selection strategy for each model was based on the following considerations: (1) relevant medical literature and clinical experience; (2) statistical significance (T-test or Chi-square test); (3) correlation (Spearman test or Pearson test); and (4) feature importance graphs. Currently, we have only developed AI prediction systems based on structured electronic medical records (non-image data). The values of the patient parameters selected were directly extracted from the HIS, which were then sent to the model prediction software , and the predicted results were returned to the system. To ensure feasibility, we did not perform complex feature extraction and fusion in the data preprocessing  in order to develop and integrate the AI software more modularly and easily. Again, to ensure feasibility, the number of features selected was generally limited to under 30.Because the ED is generally very busy, the medical staff do not often have time to use AI prediction functions. As a result, we considered encouraging the staff to actively engage in the AI system by developing a platform that helps to predict every patient’s condition automatically while working in the background. This platform allows another group of medical staff to monitor patient conditions and give timely responses if necessary. Thus, the AI dashboard design was proposed.Based on the principles and guidelines for the design and technical architecture of enterprise dashboards in health care , we propThe presentation layer focuses on the use and display of the operation interface. Easy-to-use and graphical representations were key to its design.Real-time and automated prediction:All patients are instantly scanned in the emergency room. As long as they meet the classification of the eight types of diseases, the feature values required by the model from the HIS database are automatically captured and the probability of prediction (risk) is calculated in an instant. Individuals working in the emergency room are required to have a great sense of urgency, so they experience extreme pressure. It would be unsuitable to require health practitioners in the ED to input the feature values to obtain the predicted results. Only real-time and automatic prediction can be accepted in this type of clinical practice.For example, when a patient has just been admitted to the ED and is coded as elderly with influenza by the physician (ICD9 codes as 487 or 488), the dashboard will automatically capture the patients’ 10 feature values of influenza in the elderly models  from the HIS to calculate the risk probabilities of the five outcomes  [Moreover, the AI dashboard can capture the patients’ vital signs in a timely manner using IoT technology to perform the prediction and update the prediction results at regular intervals (currently set to refresh once every 10 min).Graphical interface presentation:The prediction results are presented directly with a concise circular figure , which allows the user to view the result at a glance, helping one to respond quickly. In addition, the AI dashboard displays each feature value for reference. The results of each prediction are then kept in a line chart showing changes in the patients’ continuous risk.Interactive simulation prediction:The developed dashboard provides interactive functions, allowing users to manually adjust the feature values to simulate possible changes in risk. This helps the medical team to plan the more appropriate treatment for possible changes in condition. The interactive function is also a useful SDM (shared decision-making) tool. Through presenting scientific digits and figures, it becomes a convenient tool for healthcare staff in explaining the patient condition to the patients or their family members.We positioned the AI dashboard as a medical assistant tool used to monitor the risks of the patients in a timely manner and to simulate possible changes in outcome through the interactive functions, allowing healthcare providers to respond in time. Currently, the AI dashboard can provide risk predictions for patients diagnosed with any of the eight diseases mentioned. In the future, other features and applications of the AI dashboard can be added, such as the monitoring of patients’ waiting time for treatment.The Model Layer reveals the source of the AI dashboard’s excellent capabilities; that is, it combines all the available AI models to provide various condition prediction functions. These models are physically stored in the Model Bank. At present, eight models for the eight patient conditions, including influenza in old age, chest pain, pancreatitis, hyperglycemic crisis, dengue, pneumonia, brain trauma, and fever are available in the model layer.AI web service platform:The AI platform is a container-enabled computer server , consistRelated development tools:® to ensure that they were compatible with the existing HIS. The AI prediction models were developed on the Jupyter Notebook platform. In response to the model algorithms and analysis functions, the development platform has to be installed with the corresponding open libraries, such as TensorFlow, Scikit-Learn, and so on.In order to meet the needs of the various specifications of AI models, we used JSON (JavaScript Object Notation) as the exchange format between the web services. JSON is an open-state format and data interchange format, including that of web applications with servers. Both the web services and the web service platform were developed in Microsoft Visual Studio.NetSince 2019, the CMMC ED has developed the AI disease prediction models for the eight conditions  based on the big data recorded over the past ten years. Each type of patient has several outcome prediction models. For instance, the AI for chest pain has two prediction models  ; the AI CMMG’s HIS was fully developed by its own Department of Information Systems . After the AI dashboard test was completed, it was launched simultaneously in the three hospitals. As shown in After a month of pilot use, 10 ED staff (7 physicians and 3 nurses) completed the acceptance investigation. They were asked to answer a five-point questionnaire based on the concept of the Technology Acceptance Model (TAM) . Their aIn terms of ease of use, the respondents felt that the ED dashboard was very useful because the graphical interface is clear at a glance and the prediction does not require artificial input parameters. In terms of usefulness, since the AI dashboard can continue to assess the patients’ risk level, it permits the medical team to respond to the needs of the patients promptly, helping to improve the safety of the patients. In general, the metrics of the model performance were almost above 0.7. The physicians expressed that they could accept the AI dashboard as a tool to assist and support their clinical decisions. The respondents mentioned that they were very much willing to use this in the ED. However, they also admitted that specific benefits still require long-term observation and verification.The AI dashboard was integrated into the existing emergency system to assist physicians with their decision-making. However, physicians still had a choice and could decide whether to use AI or not . Since CMMC only encouraged physicians to use AI rather than making it mandatory, we were able to observe the differences in the outcomes of patients whose physicians used the AI prediction and those who did not. We performed a comparison based on a preliminary impact analysis after the AI was launched and implemented in the three hospitals. We found that patients treated using the AI predictions by physicians seemed to have better outcomes than patients treated without AI predictions . This inWe proposed and implemented a four-tier AI dashboard architecture for ED clinical use. We also introduced the overall AI infrastructure and outlined the steps for developing the AI prediction model with an easy-to-access EMR that can be obtained by hospitals. The concept of an AI dashboard can enable healthcare AI to actively evaluate all the patients at the ED  and can enhance the utility of AI. To our best knowledge, the present study is the first research investigation on medical informatics that designed and implemented an AI dashboard in the ED. Some previous studies had a patient-oriented dashboard implemented in the ED ,18. ThisIn accordance with the principles and guidelines obtained in this study, we pointed out how the infrastructure of a data warehouse, an AI development platform with GPU computing power, and an AI web service platform can be constructed for the effective delivery of AI predictions. The web service interactive mode we proposed provides a flexible and modular software design approach, which is conducive to the rapid expansion and migration of the AI dashboard. Therefore, the presentation of all valuable risk values of the patients along with interaction using graphical styles made the ED dashboard highly accepted by healthcare workers.This AI dashboard integrated eight healthcare AI systems and actively evaluated and notified the healthcare providers in real time about patients with a relatively higher risk of adverse outcomes. It can be used during duty briefing and then monitoring, as needed, by nurse practitioners at the emergency observation area. In this way, more healthcare AI applications for better quality care in the ED could be implemented. When a higher-risk patient is shown in red color on the AI dashboard, indicating that the AI criteria were met and that the patient has at least one adverse outcome risk higher than 50 percent, the nurse leader can visit this patient and remind the doctor of the patient’s evaluation and management. For example, one of our AI models is a chest pain AI system. If the acute myocardial infarction predictive rate is very high, real-time information can notify the primary doctor to closely monitor this patient and consult the cardiologist if the clinical prediction favors acute myocardial infarction. Another example is our pneumonia AI system. Continuous predictions of respiratory failure can be obtained because the AI model can actively gather a patient’s oxygen saturation, heart rate, and respiratory rate through a monitor. This real-time information can assist the primary doctor in re-evaluating the patient’s clinical response to treatment. Furthermore, the feature variables, except for the patient’s age and sex, can be adjusted by healthcare providers. For instance, after the initial management, when the patient’s chest pain and troponin–I improve, the primary nurses can refresh the AI model or adjust the new value of troponin–I manually. This will prompt the AI model to show a better predictive rate of acute myocardial infarction.Using an AI dashboard can make the work of the ED more efficient. The AI dashboard can quickly update the evaluation of the patients in the HIS every ten minutes and actively classify and organize high-risk patients and non high-risk patients by the font and color of their bed number . The AI systems in the dashboard include those for influenza in old age, chest pain, pancreatitis, hyperglycemic crisis, dengue, pneumonia, brain trauma, and fever. These eight diseases were chosen for the development of AI models by our ED physicians because patients diagnosed with these diseases are usually in critical conditions. After implementing these AI systems in the ED for 2 years, the mortality rates and the ICU transposition rates of influenza in old age, chest pain, pancreatitis, hyperglycemic crisis, and pneumonia were found to be lower when the AI predictive systems were used. Since the AI dashboard is user-friendly and utilizes graphics so that it can be easily understood, it can be employed by the physician as a tool for shared decision-making, by showing the graphical representations made by the AI to the patients or their families. So far, AI implementation in the ED is still scarce all over the world .This study proposed and implemented a four-tier AI dashboard architecture in the ED for clinical use. The results of our study mark an important milestone in the development of a medical AI dashboard. In addition, this study also proposed an overall AI infrastructure and outlined the steps for developing the AI prediction model with an easy-to-access EMR in hospitals. We provided clear guidance for small- and medium-sized hospitals that intend to develop AI applications but do not know where to start. Therefore, this study is deeply aligned with academic and practical contributions.There were some limitations of the study. Firstly, all of the big data of ED patients were gathered from three branch hospitals of CMMC. Thus, further external validation of the AI models in a diverse hospital is warranted. Moreover, future studies should encourage more hospitals to cooperate so that the study’s generalizability can be extended through federated learning  or otherThe current AI applications developed by CMMC were mainly based on structuralized electronic medical records. Medical image AI and unstructured EMR AI are important areas and need to be studied and implemented in the future, regardless of whether this concerns individual predictions or group predictions in the dashboard form. Moreover, we call for more AI models for various diseases in the ED to be developed to maximize the coverage of monitored patients. In addition, discussing the success of medical AI at the the environmental and organizational level is a very worthy research topic, including the topics of policy trend driving, value-added and benefit assessment, harmony between stakeholders, and the consensus of the organization ,23.Currently, we are planning to develop the AI cloud service platform and share our ED AI services with medical institutions in remote villages and outlying islands. For example, the physicians in a rural hospital can send their ED chest pain patients’ feature variable values through the Internet to the CMMC AI service platform, and the platform can then return the real-time risk predictions. In so doing, the medical team of a tertiary-level hospital can provide assistance and high-quality support to hospitals lacking resources anytime and anywhere. Through AI, the medical digital gaps will be filled.