Construction and optimization of traditional Chinese medicine constitution prediction models based on deep learning

Objective To cater to the demands for personalized health services from a deep learning per-spective by investigating the characteristics of traditional Chinese medicine(TCM)constitu-tion data and constructing models to explore new prediction methods.Methods Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21,2020,to April 6,2022.The data were used to identify nine TCM constitutions,including balanced constitution,Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,phlegm dampness constitution,damp heat constitution,stagnant blood constitution,Qi stagnation constitution,and specific-inherited predisposition constitution.Deep learning algorithms were employed to construct multi-layer perceptron(MLP),long short-term memory(LSTM),and deep belief network(DBN)models for the prediction of TCM constitutions based on the nine constitution types.To optimize these TCM constitution prediction models,this study in-troduced the attention mechanism(AM),grey wolf optimizer(GWO),and particle swarm op-timization(PSO).The models’performance was evaluated before and after optimization us-ing the F1-score,accuracy,precision,and recall.Results The research analyzed a total of 31655 pieces of data.(i)Before optimization,the MLP model achieved more than 90%prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions.The LSTM model's prediction accuracies exceeded 60%,indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data.Regarding the DBN model,the binary classification analysis showed that,apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution,with accuracies of 65%and 60%,respectively.The DBN model demonstrated considerable discriminative power for other constitution types,achieving prediction accuracy rates and area under the receiver op-erating characteristic(ROC)curve(AUC)values exceeding 70%and 0.78,respectively.This indicates that while the model possesses a certain level of constitutional differentiation abili-ty,it encounters limitations in processing specific constitutional features,leaving room for further improvement in its performance.For multi-class classification problem,the DBN model’s prediction accuracy rate fell short of 50%.(ii)After optimization,the LSTM model,enhanced with the AM,typically achieved a prediction accuracy rate above 75%,with lower performance for the Qi deficiency constitution,stagnant blood constitution,and Qi stagna-tion constitution.The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%,while the PSO-optimized model had a decreased accuracy rate to 37%.The GWO-PSO-DBN model,optimized with both algorithms,demon-strated an improved prediction accuracy rate of 54%.Conclusion This study constructed MLP,LSTM,and DBN models for predicting TCM consti-tution and improved them based on different optimisation algorithms.The results showed that the MLP model performs well,the LSTM and DBN models were effective in prediction but with certain limitations.This study also provided a new technology reference for the es-tablishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Contrastive Study of Learning by APPs and Traditional Classroom Learning Among College Students

In recent years, a large number of college students are using educational APPs to learn English. The author has deeply analyzed and explored the difference of learning by APPs and traditional classroom learning with the background of Constructivism. Learning by APPs and traditional classroom learning have their prospective advantages and disadvantages on learning time,space, contents, methods, efficiency and supervision. Learners can make full use of educational APPs, combining APPs with traditional classroom learning to realize blending learning and achieve high-efficiency.

The Overview of Database Security Threats’ Solutions: Traditional and Machine Learning

As an information-rich collective, there are always some people who choose to take risks for some ulterior purpose and others are committed to finding ways to deal with database security threats. The purpose of database security research is to prevent the database from being illegally used or destroyed. This paper introduces the main literature in the field of database security research in recent years. First of all, we classify these papers, the classification criteria are the influencing factors of database security. Compared with the traditional and machine learning (ML) methods, some explanations of concepts are interspersed to make these methods easier to understand. Secondly, we find that the related research has achieved some gratifying results, but there are also some shortcomings, such as weak generalization, deviation from reality. Then, possible future work in this research is proposed. Finally, we summarize the main contribution.

Exploring the Application Effect of Flipped Classroom Combined with Problem-Based Learning Teaching Method in Clinical Skills Teaching of Standardized Training for Resident Doctors of Traditional Chinese Medicine

Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.

Inheritance and Innovation of Traditional Chinese Culture from the Perspective of Mutual Learning among Civilizations

General Secretary Xi Jinping has proposed the new civilization concept of civilization exchange and mutual learning,and the high cultural self-confidence lies in deep civilization exchange and mutual learning.Chinese traditional culture is the concentrated expression of country and nation at the cultural and spiritual level.Under the background of civilization mutual learming,it should cultivate the ideological foundation of traditional culture,focus on diversified development of media,build a bridge of communication between countries,and finally realize the construc-tion of the human destiny community and cultural community of“beauty representing itself with diversity and integri-ty”between Chinese traditional culture and other cultures.

Research trends of machine learning in traditional medicine:a big-data based tenyear bibliometric analysis

Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field.Methods:Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database.Bibliometric analysis and visualization were performed using the Bibliometrix package in R software.Global trends,source journals,authorship,and thematic keywords analysis were performed in this study.Results:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and analyzed.The average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).IEEE Access had the largest number of published articles,with a total of 15 articles.Calvin Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this field.Shanghai University of Traditional Chinese Medicine and University of California,San Francisco were the main research institutions.Conclusion:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field.According to current global trends,the number of publications in this field will gradually increase.China currently dominated the field.Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention.

Analysis of microblog public opinion characteristics on traditional Chinese medicine against COVID-19 based on deep learning

The opinion research on traditional Chinese medicine during the coronavirus disease 2019(COVID-19)pandemic on microblog,a social network,took into account the national people’s fight against COVID-19—the research background—the strength of traditional Chinese medicine during the pandemic—the research topic—and the public opinion—the research object.The timeline was divided into three stages according to the overall heat change.In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine during the different stages of the pandemic,deep learning analysis methods such as emotional analysis and Latent Dirichlet Allocation analysis were used.This study found that the public’s positive“emotional composition”on traditional Chinese medicine significantly improved within the timeline,while the public’s autonomy was enhanced and the overall public opinion started to show an increased trend.

Effective Face-to-faceTutorials of Online English Learning Lie in Interaction of Affective Domain and Higher Levels of Cognitive Domain of Bloom＇s Taxonomy

Online English learning as an outcome of the rapid development of the Internet has got a wider and wider market in China. However, problems of varieties have also occurred along its way. People never stop thinking of better strategies either in designing online course wares or tutorials to help smooth the learning process. My experience as a tutor is that interaction of affective domain and higher levels of cognitive domain of Bloom＇s Taxonomy plays an important role in face-to-face tutorials of online English learning.

Problem-Based Learning(PBL)Teaching Design in Enhancing College Students’Innovation Ability:Taking the Traditional Dress Design Course as An Example

This paper combines the cultivation of innovation ability with the content of problem-based learning(PBL),analyzes the current situation of the traditional dress design course,discusses the problems existing in the cultivation of innovation ability of college and university traditional dress design,and searches for the strategies to improve students’innovation ability based on PBL.This paper argues that PBL can provide assistance to the teaching design of traditional dress design courses,which is conducive to improving students’innovation ability in traditional dress design and realizing the desired teaching effect.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

The Impact of Music Education on Children’s Cognitive Development:From Traditional Classrooms to Modern Digital Platforms

Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and adaptability.This article synthesizes research data to examine the cognitive benefits of music instruction,including increased IQ,language proficiency,memory,and attention.Traditional face-to-face training,while personalized and socially interactive,faces limitations such as budget constraints and accessibility.Modern digital platforms offer individualized learning paths with AI-driven feedback but may lack necessary interpersonal interaction.This paper proposes a hybrid approach to music education,integrating traditional and digital methods to maximize cognitive gains.Further research is recommended to explore the implementation of these integrated learning strategies in varied educational settings.

Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals

A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Historical data, including daily prices and trading volumes, are employed to implement models such as Long Short Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Autoregressive Integrated Moving Average (ARIMA), and Autoregressive Moving Average (ARMA). These models are assessed over three-time horizons: short-term (1 year), medium-term (2.5 years), and long-term (5 years), with performance measured by Mean Squared Error (MSE) and Mean Absolute Error (MAE). The stability of the time series is tested using the Augmented Dickey-Fuller (ADF) test. Results reveal that deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data, resulting in more accurate predictions. However, these models require greater computational resources and offer less interpretability than traditional approaches. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies. Future research could incorporate external factors such as social media sentiment and economic indicators, refine model architectures, and explore real-time applications to enhance prediction accuracy and scalability.

Application of Traditional Chinese Culture in Cross-Cultural English Education:A Case Study of the Maritime Silk Road in Guangdong Province

The phenomenon of aphasia in Chinese culture is serious.The existing English teaching materials emphasize too much Western culture education and lack traditional Chinese cultural elements.Therefore,this paper takes the Guangdong Maritime Silk Road as an example to study the specific application of traditional Chinese culture in cross-cultural English education.This paper first summarizes the significance of cross-cultural integration into college English education and then points out the serious phenomenon of Chinese cultural aphasia.Next,the paper focuses on English education,using English textbooks as a starting point to explore and integrate strategies related to excellent traditional Chinese culture from the Guangdong Maritime Silk Road.By integrating traditional Chinese culture into business English classes(with the Guangdong Maritime Silk Road as an example),the study explores the influence of such cultural integration on students’cross-cultural communication skills,cultural identity,and learning effects.The results showed that the P value of the experimental group and the control group was<0.05,that is,cultural integration had a positive effect on improving the effect of cross-cultural English education.The overall scores and cultural confidence of the experimental group are higher than those of the control group,which proves that cross-cultural teaching has a positive effect on the improvement of students’scores.

Emotional Perceptions on Environmental Elements:A Case Study on Traditional Villages Based on Online Reviews and Machine Learning

For traditional villages,evaluating one’s emotional perceptions on their environment is of great significance for promoting their sustainable development.Through a case study on traditional villages in China,this research developed a quantitative approach for studying the influence of environmental elements on emotional perceptions in a wide scope.76 traditional villages across China were selected as cases and the online reviews on these villages were analyzed.Through natural language processing,the emotional perceptions expressed in these reviews were qualified,the environmental elements mentioned in these reviews were extracted,and a lexicon of 14 environmental elements classified into the three categories of settlement,nature,and culture was developed.The correlation between environmental elements and emotional perceptions was then evaluated by measuring perception frequency and perception satisfaction.The in-depth analysis shows that people generally have a positive attitude toward traditional villages.Settlement elements,such as“house”and“public building,”are more frequently perceived than natural and cultural elements,while the latter ones play a positive influence on the emotional perception of reviewers on traditional villages.The methods developed in this research can be used to support the policy-making of protecting and revitalizing traditional villages to balance heritage protection and village development.

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

E-Learning环境下的中医药网络平台的研究

E-Learning(网络学习)作为是一种全新的学习方式,它利用互联网的数据共享、快速传输等特点和信息科技相结合,进行内容传播和快速学习,越来越为各行各业所青睐。中医药网络平台则利用E-Learning的方式,结合大量数字中医药研究数据,建立具有中医药特色的网络平台。结合医、教、研、学的特点,为学生提供一个学习工具,增加学生与教师互动交流,同时也为研究人员、临床医生搭建一个协作交流的平台。

Eleutheroside B or E enhances learning and memory in experimentally aged rats

Eleutheroside B or E, the main component of Acanthopanax, can relieve fatigue, enhance memory, and improve human cognition. Numerous studies have confirmed that high doses of acetylcholine significantly attenuate clinical symptoms and delay the progression of Alzheimer＇s disease. The present study replicated a rat model of aging induced by injecting quinolinic acid into the hippocampal CA1 region. These rats were intraperitoneally injected with low, medium and high doses of eleutheroside B or E （50, 100, 200 mg/kg）, and rats injected with Huperzine A or PBS were used as controls. At 4 weeks after administration, behavioral tests showed that the escape latencies and errors in searching for the platform in a Morris water maze were dose-dependently reduced in rats treated with medium and high-dose eleutheroside B or E. Hematoxylin-eosin staining showed that the number of surviving hippocampal neurons was greater and pathological injury was milder in three eleutheroside B or E groups compared with model group. Hippocampal homogenates showed enhanced cholinesterase activity, and dose-dependent increases in acetylcholine content and decreases in choline content following eleutheroside B or E treatment, similar to those seen in the Huperzine A group. These findings indicate that eleutheroside B or E improves learning and memory in aged rats. These effects of eleutheroside B or E may be mediated by activation of cholinesterase or enhanced reuse of choline to accelerate the synthesis of acetylcholine in hippocampal neurons.

Radix Achyranthis Bidentatae improves learning and memory capabilities in ovariectomized rats

Kidney-tonifying recipe can reduce the accumulation of advanced glycation end products, prevent neuronal degeneration and improve cognitive functions in ovariectomized rats. Radix Achyranthis Bidentatae alcohol extracts may dose-dependently inhibit non-enzymatic saccharification in vitro. This study aimed to examine the effect of Radix Achyranthis Bidentatae on advanced glycation end products and on learning and memory capabilities in ovariectomized rats. Ovariectomized rats were treated with Radix Achyranthis Bidentatae alcohol extracts （containing 1.5 g/kg crude drug） or 0.1% aminoguanidine for 12 weeks and behavioral testing was performed with the Y-electrical maze. This test revealed that Radix Achyranthis Bidentatae and aminoguanidine could improve the learning and memory capabilities of ovariectomized rats. Results of competitive enzyme-linked immunosorbent assay showed that treatment with Radix Achyranthis Bidentatae or aminoguanidine reduced the accumulation of advanced glycation end products in the frontal cortex of ovariectomized rats, while increasing content in the blood and urine. Biochemical tests showed that treatment with Radix Achyranthis Bidentatae or aminoguanidine decreased superoxide dismutase activity in the serum and frontal cortex, and increased serum levels of glutathione peroxidase in ovariectomized rats. In addition there was no apparent effect on malondialdehyde levels. These experimental findings indicate that Radix Achyranthis Bidentatae inhibits production of advanced glycation end products and its accumulation in brain tissue, and improves learning and memory capabilities in ovariectomized rats. These effects may be associated with an anti-oxidative action of the extract.

Wendan decoction improves learning and memory deficits in a rat model of schizophrenia

An experimental model of schizophrenia was established using dizocilpine （MK-801）. Rats were intragastrically administered with Wendan decoction or clozapine for 21 days prior to establishing the model. The results revealed that the latency of schizophrenia model rats to escape from the hidden platform in the Morris water maze was significantly shortened after administration of Wendan decoction or clozapine. In addition, the treated rats crossed the platform significantly more times than the untreated model rats. Moreover, the rate of successful long-term potentiation induction in the Wendan decoction group and clozapine group were also obviously increased compared with the model group, and the population spike peak latency was significantly shortened. These experimental findings suggest that Wendan decoction can improve the learning and memory ability of schizophrenic rats to the same extent as clozapine treatment.