An interpretability model for syndrome differentiation of HBV-ACLF in traditional Chinese medicine using small-sample imbalanced data

Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.

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.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Classification of Adversarial Attacks Using Ensemble Clustering Approach

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of attacks.To safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known intrusions.There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive traffics.The current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network applications.In contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class labels.These ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their combinations.Based on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the literature.This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated intrusions.In addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

Systematic review of digital twin technology and applications

As one of the most important applications of digitalization,intelligence,and service,the digital twin(DT)breaks through the constraints of time,space,cost,and security on physical entities,expands and optimizes the relevant functions of physical entities,and enhances their application value.This phenomenon has been widely studied in academia and industry.In this study,the concept and definition of DT,as utilized by scholars and researchers in various fields of industry,are summarized.The internal association between DT and related technologies is explained.The four stages of DT development history are identified.The fundamentals of the technology,evaluation indexes,and model frameworks are reviewed.Subsequently,a conceptual ternary model of DT based on time,space,and logic is proposed.The technology and application status of typical DT systems are described.Finally,the current technical challenges of DT technology are analyzed,and directions for future development are discussed.

An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

The lightweight encryption algorithm based on Add-Rotation-XOR(ARX)operation has attracted much attention due to its high software affinity and fast operation speed.However,lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm.The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost.This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes.Firstly,we specify a hierarchical security framework by quantitatively evaluating the indicators based on side-channel attacks.Then,optimize the masking algorithm to achieve a trade-off balance by leveraging the software-based local masking strategies and non-full-round masking strategies.Finally,refactor the assembly instruction to improve the leaks by exploring the leakage cause at assembly instruction.To illustrate the feasibility of the proposed scheme,we further conducted a case study by designing a software-based masking method for Chaskey.The experimental results show that the proposed method can effectively weaken the impact of physical attacks.

FLAWED NARRATIVES OF CHINA AND TECHNOLOGY HARM AMERICAN AUDIENCES

The back half of the 20th century belonged to the United States.One reason is that America’s colleges and universities produced undergraduate,graduate,and doctoral graduates who,with increasing rapidity and sophistication,turned out one new technology tool after another.Using these and other advantages,the U.S.became the world’s powerhouse,outpacing its allies and adversaries.

Vehicle Head and Tail Recognition Algorithm for Lightweight DCDSNet

In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance.Therefore,obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research.In this paper,a lightweight densely connected,and deeply separable convolutional network(DCDSNet)algorithmis proposed to achieve this goal.Visual Geometry Group(VGG)model is improved by utilizing the convolution instead of the fully connected module,the deeply separable convolution module,and the densely connected network module,with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters.The algorithm achieves better results in the mine vehicle recognition dataset.Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19.

Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

A Multilayer Network Constructed for Herb and Prescription Efficacy Analysis

Chinese Medicine(CM)has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’symptoms and syndromes.However,the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall functions.Network analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM prescriptions.To illustrate their connections and correlations,prescription function(PF),prescription herb(PH),and herbal efficacy(HE)intranetworks are proposed based on CM theory to identify relationships between herbs and prescriptions.These three networks are then connected by PF-PH and PH-HE interlayer networks adopting herb dosage to form a multidimensional heterogeneous network,a Prescription-Herb-Function Network(PHFN).The network is applied to 112 classic prescriptions from Treatise on Exogenous Febrile and Miscellaneous Diseases to illustrate the application of PHFN.The PHFN is constructed including 146 functions in PF intra network,89 herbs in the PH intra network,and 163 herbal efficacies in the HE intra network.The results show that herb pairs with synergistic actions have stronger relevance,such as licorice-cassia twig,licorice-Chinese date,fresh ginger-Chinese date,etc.The integration of dosage to the network helps to indicate the main herbs for cluster analysis and automatic formulation.PHFN also reveals the internal relationships between the functions of prescriptions and composed herbal efficacies.

Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.

Modeling and Design of Real-Time Pricing Systems Based on Markov Decision Processes

A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.

On the Harmonic Index of Triangle-Free Graphs

The harmonic index of a graph?G? is defined as where d(u) denotes the degree of a vertex u in G . In this work, we give another expression for the Harmonic index. Using this expression, we give the minimum value of the harmonic index for any triangle-free graphs with order n and minimum degree δ ≥ k for k≤ n/2? and show the corresponding extremal graph is the complete graph.

Change Detection of Rice Cultivation in Bangladesh Based on the Phenological Analysis of MODIS Data

Mapping rice cultivation is indispensable for monitoring food supply conditions in Bangladesh because of the economical importance of the crop for supporting ever increasing population in the country. In this paper, we extract the rice paddy field using the MODIS satellite data for five districts of Pabna, Manikganj, Sherpur, Sylhet, and Gazipur, each of which is characterized with its own aspects in terms of rice cultivation. Land classification is implemented using the vegetation index information derived from the red (band 1) and near-infrared (band 2) bands of MODIS 8-day composite time series data for the two time periods of 2001-2003 and 2011-2013. Results of unsupervised classification indicate that the paddy area coverage increased about 4% and 1% in Gazipur and Sylhet, respectively. In Pabna, Manikganj, and Sherpur, on the other hand, paddy area decreased by 10%, 2% and 5%, respectively, whereas notable increase of 12%, 2% and 7% was found in homestead area coverage, which is becoming more and more important for better management of small-scale agroforestry. At the same time, in Sherpur and Sylhet, forest area increased by 1% and 2% over the same time period. As a validation of these results, the changes detected in Gazipur are compared with those previously derived from the analysis of Landsat data with higher spatial resolution of 30 m as compared with that of MODIS (250 m). Also, the seasonal rice cropping pattern is studied in these five districts for discriminating cultivated rice types. These changes suggest that as a whole, efforts are being made to increase the food production, though the influence of population pressure and economic growth is apparent in these regions.

Mathematical Modeling of Carcinogenesis Based on Chromosome Aberration Data

Objective： The progression of human cancer is characterized by the accumulation of genetic instability. An increasing number of experimental genetic molecular techniques have been used to detect chromosome aberrations. Previous studies on chromosome abnormalities often focused on identifying the frequent loci of chromosome alterations, but rarely addressed the issue of interrelationship of chromosomal abnormalities. In the last few years, several mathematical models have been employed to construct models of carcinogenesis, in an attempt to identify the time order and cause-and-effect relationship of chromosome aberrations. The principles and applications of these models are reviewed and compared in this paper. Mathematical modeling of carcinogenesis can contribute to our understanding of the molecular genetics of tumor development, and identification of cancer related genes, thus leading to improved clinical practice of cancer.

Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications.However,users’personal privacy will be leaked in the mining process.In recent years,application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method.Local differential privacy means that users first perturb the original data and then send these data to the aggregator,preventing the aggregator from revealing the user’s private information.We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s multi-attribute.The main technique has bitmap encoding for converting the user’s original data into a binary string.It also includes how to choose the best perturbation algorithm for varying user attributes,and uses the frequent pattern tree(FP-tree)algorithm to mine frequent itemsets.Finally,we incorporate the threshold random response(TRR)algorithm in the framework and compare it with the existing algorithms,and demonstrate that the TRR algorithm has higher accuracy for mining frequent itemsets.

A Support System of Sightseeing Tour Planning Using Public Transportation in Japanese Rural Areas

Though most of tourists tend to visit multiple sightseeing spots during their sightseeing tours,it is difficult for them to efficiently acquire information necessary for their sightseeing tour planning.Additionally,in rural area,many people hope to use public transportation which has not developed as much as in urban areas.The present study aims to design and develop a support system of sightseeing tour planning in Japanese rural areas,adopting the information related to real timetables of public transportation on both the sea and the ground,and genetic algorism(GA).The system was developed integrating moving route recommendation system,web-geographic information systems(Web-GIS),and augmented reality(AR)application.Furthermore,Kagawa Prefecture in the western part was selected as the operation target area.The operation of the system was conducted for two months,targeting those inside and outside the operation target area,and web questionnaire surveys were conducted.From the evaluation results based on the web questionnaire surveys,the usefulness of all the original functions as well as of the entire system was analyzed.Additionally,though some users could not easily use the system,it is expected that they will get used to utilizing it by their continuous use.

Research on knowledge reasoning of TCM based on knowledge graphs

With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.