Network Data Mining Research Articles

Analysis of the medication rules of traditional Chinese medicines (TCMs) in treating liver cancer and potential TCMs exploration

BackgroundLiver cancer (LCa) is one of the most common malignancies. Western medicine is the main treatment strategy of LCa, but it has not achieved the ideal therapeutic effect. Traditional Chinese Medicines (TCMs) have a wide range of biological effects, lots of evidence suggests that they may kill tumors by regulating the tumor microenvironment and enhancing the immune system. ObjectThe purpose of this research is to explore the medication rules of TCMs in treating LCa and excavate the potential TCMs for LCa. MethodsWe combined genes associated with the MAPK, PI3K/Akt, and mTOR signaling pathways, as well as differentially expressed genes of LCa from the TCGA database, to determine 12 prospective core targets by building a prognostic model. We added 18 previously published LCa core targets to create a list of key targets. GO and KEGG analyses were used to validate the rationality of the key targets. The reliability of the corresponding relationships between targets and compounds was verified by molecular docking. The targets-compounds-herbs network was constructed and topologically analyzed to obtain potential herbs for LCa. The nature, flavor and meridian tropism of the herbs were analyzed to explore the rule of TCMs in the treatment of LCa. ResultsWe obtained 30 potential targets including BRCA1, STMN1, MECOM, ITGB5 and SKP2. A total of 115 candidate compounds including resveratrol, quercetin, epigallocatechin gallate, estradiol and curcumin, and 1182 candidate herbs, including Jinyinhua, Gouguye, Tufuling, Huzhang, Jishiteng, Nvzhenzi, Sangbaipi and Qianhu were obtained. The vast majority of LCa's core targets, compounds and herbs we obtained have been verified by previous studies. Among the potential herbs, the categories of clearing heat and detoxifying, activating blood and dissolving stasis, and strengthening the spleen and benefiting the qi accounted for a high percentage. The herbs against LCa are mainly bitter, pungent, sweet in flavor, warm, cold, and mild in nature, and are mainly distributed in the liver, stomach, and lung meridians. ConclusionsSome of the potential herbs we obtained like Jinyinhua, Gouguye, Tufuling, Huzhang and Qianhu have been proved to be effective for LCa, while some of them like Jishiteng, Nvzhenzi and Sangbaipi have been shown to have a protective effect on the liver. The potential herbs have not been widely and deeply used and need to be further explored. The combination of network pharmacology and data mining provides novel approaches to the screening and exploitation of TCM.

Network Representation Learning Algorithm Based on Community Folding

<p>Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space, which can reduce the temporal and spatial complexity of downstream network data mining such as node classification and graph clustering. This paper addresses the problem that neighborhood information-based network representation learning algorithm ignores the global topological information of the network. We propose the Network Representation Learning Algorithm Based on Community Folding (CF-NRL) considering the influence of community structure on the global topology of the network. Each community of the target network is regarded as a folding unit, the same network representation learning algorithm is used to learn the vector representation of the nodes on the folding network and the target network, then the vector representations are spliced correspondingly to obtain the final vector representation of the node. Experimental results show the excellent performance of the proposed algorithm.</p> <p> </p>

Clustering Optimization Algorithm for Data Mining Based on Artificial Intelligence Neural Network

Social production and life have become increasingly prominent. Cluster analysis is the basis for further processing of the data. The concept of data mining and the application of neural networks in data mining are introduced. According to the related technology of data mining, this article introduces in detail the two-layer perceptron, backpropagation (BP) neural network, RBF radial basis function network for processing classification problems, and self-organizing map (SOM) self-organizing neural network for unsupervised clustering problems. According to the characteristics of self-adaptive and self-organizing capabilities of these algorithms, we learn and design and implement data mining clustering optimization algorithms. In this paper, the neural network-based data mining process consists of three stages: data preparation, rule extraction, and rule evaluation. This paper studies the teaching-type and decomposition-type rule extraction algorithms. After analyzing the BP decomposition-type algorithm, the correlation method is used to calculate the correlation of the input and output neurons. After sorting by the degree of correlation, the RBF neural network is used for node selection. This can greatly reduce the number of input nodes of the neural network, simplify the network structure, reduce the number of recursive splits of the subnet, and improve calculation efficiency. Taking the model as an example, the training error is calculated through data mining technology and clustering algorithm. Data mining clustering optimization algorithm mainly improves the popular neural network from two aspects: finer model design and model pruning, and simulates model complexity, computational complexity, and errors through simulation experiments. The rate is measured, and finally, the simulation experiment is performed. The results show that the proposed algorithm for differential distributed data mining has higher accuracy and stronger convergence ability and overcomes the shortcomings and shortcomings of several original genetic algorithm optimization neural network data mining models; it can effectively improve the searchability and search accuracy of the algorithm and improve the efficiency of data mining. Accuracy and accuracy have a wide range of applications.

Data Mining of Regional Economic Analysis Based on Mobile Sensor Network Technology

With the continuous development of regional economy, the difference of regional economy has also aroused the attention of all walks of life. Due to the limitations of the traditional research methods, the research results are relatively simple and unable to conduct a more comprehensive analysis. The traditional methods include the following: (1) analyze the evolution of regional logistics based on the location Gini coefficient and location quotient of GIS, and reflect the situation of industrial agglomeration from the annual change curve of the location Gini coefficient; (2) use SPSS12.0 software to perform multivariate or event factors, and analyze and calculate the factor score to sum up several principal component factors; and (3) the production function analysis method is used to measure the economies of scale and agglomeration. As an extension, the relationship between the estimated total output and the agglomeration index of the factor input to measure the uniform state of the industrial distribution department is an effective measurement method for the agglomeration economy. In order to promote the sustainable development of regional economy, this paper analyzes the regional economy comprehensively based on the emerging mobile sensor network technology and data mining technology. Firstly, this paper analyzes the location technology of mobile sensor networks based on sequential Monte Carlo, selects the C -means clustering method which is suitable for economic large-sample clustering analysis, and constructs a complete data mining model. Then, the model is used to analyze the economic, social, natural, and educational science and technology indicators of a certain region from 2015 to 2019. The results show that the first principal component weight of economic indicators is the highest proportion of fiscal revenue, which is 0.986. This shows that the role of fiscal revenue in economic indicators is greater. The main index of urban consumption is 72.0, which is the highest. This shows that the population growth rate and the average consumption of urban households in social indicators play a greater role. The first principal component of natural index has the highest weight of pollution emission, which is 0.47, while the second principal component has the highest weight of total energy consumption, which is 0.48. This shows that the pollution emissions and total energy consumption in the natural indicators play a greater role. In the educational science and technology index, the first principal component weight is the highest, which is 0.61. This shows that the education funds play an important role in educational science and technology indicators. Therefore, the data mining model based on mobile sensor network technology can comprehensively and accurately analyze various indicators of regional economy.

Attention-Based Cross-Modality Feature Complementation for Multispectral Pedestrian Detection

Multispectral pedestrian detection based on deep learning can provide a robust and accurate detection under different illumination conditions, which has important research significance in safety. In order to reduce the log-average miss rate of the object under different illumination conditions, a new one-stage detector suitable for multispectral pedestrian detection is proposed. First, in order to realize the complementarity between the information flows of the two modalities in the feature extraction stage to reduce the object loss, a low-cost cross-modality feature complementary module (CFCM) is proposed. Second, in order to suppress the background noise in different environments and enhance the semantic information and location information of the object, so as to reduce the error detection of the object, an attention-based feature enhancement fusion module (AFEFM) is proposed. Thirdly, through the feature complementarity of color-thermal image pair and the multi-scale fusion of depth feature layer, the horizontal and vertical multi-dimensional data mining of parallel deep neural network is realized, which provides effective data support for object detection algorithm. Finally, through the reasonable arrangement of proposed modules, a robust multispectral detection framework is proposed. The experimental results on the Korea Advanced Institute of Science and Technology (KAIST) pedestrian benchmark show that the proposed method has the lowest log-average miss rate compared with other state-of-the-art multispectral pedestrian detectors, and has a good balance in speed and accuracy.

Online Shopper Intention Analysis Using Conventional Machine Learning And Deep Neural Network Classification Algorithm

The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.

Design and Implementation of Rainfall Prediction Model using Supervised Machine Learning Data Mining Technique s

Data mining is a rapidly developing technology that has enriched a lot of field such as business analysis, market analysis, weather forecasting, stock market analysis and many more. It starts with collecting data sets from reliable sources and pre-processing that data. There are some anomalies associated with data collected in large volumes such as outliers, missing values, and duplicated values. Remove these kinds of anomalies is teamed as pre-processing of data. In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ANN classification techniques is a complex data mining technique results in high accuracy in prediction of rainfall.

Design and Implementation of Rainfall Prediction Model using Supervised Machine Learning Data Mining Techniques

Data mining is a rapidly developing technology that has enriched a lot of field such as business analysis, market analysis, weather forecasting, stock market analysis and many more. It starts with collecting data sets from reliable sources and pre-processing that data. There are some anomalies associated with data collected in large volumes such as outliers, missing values, and duplicated values. Remove these kinds of anomalies is teamed as pre-processing of data. In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ANN classification techniques is a complex data mining technique results in high accuracy in prediction of rainfall.

The Relationship on Exercise Anticipation, Function and Self-efficacy in Patients After Knee Surgeries Over Six Months: An Observational Study.

AimTo explore the association between the exercise anticipation, function and self-efficacy in patients after knee surgeries over six months and to identify the predictors for rehabilitation.MethodsThis observational study used the cross-sectional survey method and was divided into two stages: (1) designing the experimental video materials and answer cards about exercise anticipation, and (2) analyzing the correlation of the level of exercise anticipation, knee function, modified gait efficacy scale (mGES), self-efficacy for rehabilitation outcome scale (SER), self-efficacy for exercise scale (SEE), time up and go task (TUG) and knee joint angle in walking. We used IBM SPSS modeler 18.0 software for establishing the Bayesian network data mining model, which can identify strong positive and negative associations.ResultsA total of 110 patients were included in this study. Our research confirmed that the exercise anticipation score exhibited the significant correlation with SER (r=0.552, P<0.01), SEE (r=0.457, P<0.01), TUG (r=−0.419, P<0.01) and knee joint angle in walking (r=−0.342, P<0.01). There is only one parent node of exercise expectation, that is, rehabilitation self-efficacy, which shows that the rehabilitation self-efficacy of patients after knee surgery is directly related to their ability to judge exercise expectation. Meanwhile this study verified the model results, and the area under the ROC curve (AUC) is 0.572 (755/1320), indicating that the prediction performance is acceptable.ConclusionThis preliminary study confirmed that improving the exercise anticipation and rehabilitation self-efficacy is an important method of enhancing the knee function in patients after knee surgeries over six months. In the future, the long-term effect of the exercise anticipation and self-efficacy should be researched.

Network Analysis of Herbs that are Frequently Prescribed for Osteoporosis with a Focus on Oasis Platform Research

Objectives: This study analyzed, through network analysis and data mining analysis, the relationship between herbs used in osteoporosis prescriptions, diversified the analysis of osteoporosis-related prescriptions, and analyzed the combination of herbs used in osteoporosis-related prescriptions.Methods: The prescriptions used in osteoporosis treatment and experiments were established by conducting a full survey of the papers published by the OASIS site. A database for osteoporosis-related prescriptions was established, herbs were extracted, and the frequency of frequent herbs and prescriptions were investigated using Excel (MS offices ver. 2013). Using the freeware R version 4.0.3 (2020-10-10), igraph, and arules package, network analysis was performed in the first second of prescription composition.Results: Among the osteoporosis-related prescriptions, the most studied prescriptions are as follows.: &lt;i&gt;Yukmijihwang-tang&lt;/i&gt; (六味地黃湯) and &lt;i&gt;Samul-tang&lt;/i&gt; (四物湯). In the osteoporosis prescription network, herbs with connection centrality, proximity centrality, mediation centrality, and eigenvector centrality appeared in the order of Rehmanniae Radix Preparata, Angelicae Gigantis Radix, Poria Sclerotium, Paeoniae Radix, and Glycyrrhizae Radix et Rhizoma. After extracting the herbal combination network, including the corresponding herbs, and clustering it, it can be divided into drugs of the &lt;i&gt;Yukmijihwang-tang&lt;/i&gt; (六味地黃湯) series and the &lt;i&gt;Samul-tang&lt;/i&gt; (四物湯).Conclusions: This study could assist researchers in diversifyingy formula analysis in future studies. Moreover, the herbal combination used in osteoporosis prescriptions could be used to search for osteoporosis prescriptions in other databases or to create a new prescription.

Design and Development of Ternary-Based Anomaly Detection in Semantic Graphs Using Metaheuristic Algorithm

At present, the field of homeland security faces many obstacles while determining abnormal or suspicious entities within the huge set of data. Several approaches have been adopted from social network analysis and data mining; however, it is challenging to identify the objective of abnormal instances within the huge complicated semantic graphs. The abnormal node is the one that takes an individual or abnormal semantic in the network. Hence, for defining this notion, a graph structure is implemented for generating the semantic profile of each node by numerous kinds of nodes and links that are associated to the node in a specific distance via edges. Once the graph structure is framed, the ternary list is formed on the basis of its adjacent nodes. The abnormalities in the nodes are detected by introducing a new optimization concept referred to as biogeography optimization with fitness sorted update (BO-FBU), which is the extended version of the standard biogeography optimization algorithm (BBO). The abnormal behavior in the network is identified by the similarities among the derived rule features. Further, the performance of the proposed model is compared to the other classical models in terms of certain performance measures. These techniques will be useful to detect digital crime and forensics.

Global scientific collaboration: A social network analysis and data mining of the co-authorship networks

Co-authorship networks consist of nodes and numerous links indicating scientific collaboration of researchers. These networks could be studied through social networks analysis and data mining techniques. The focus of the article is twofold: the first objective is the analysis of the co-authorship networks of the top 60 countries that had the highest number of scientific publications in the world, and the second one is the discovery of collaboration patterns of highly cited papers of these countries. To do so, all scientific publications of the top 60 countries in all fields as well as their highly cited papers were included in the study period between 2011 and 2015. The research samples in the first part included 10,460,999 documents and in the second part encompassed 711,025 highly cited papers. Required data were extracted from web of science database. To analyse co-authorship networks, centrality indices and clustering coefficient were used. UCINET, Pajek, VOSviewer and BibExcel software were used to map co-authorship networks of the countries and to calculate indices. Finally, the discovery of collaboration patterns in highly cited papers is studied through association rules. The research data indicated that over 95% of documents has been produced by the top 60 countries. In addition, the USA, Germany, England, France and Spain launched the most co-authorship. Quantitatively, there have been the most powerful collaboration links between China and the USA, the USA and England, the USA and Germany, and the USA and Canada. The clustering data indicated that collaborations of the top countries of the world were in three main clusters. The Friedman test showed that there was a significant difference in the priorities of the countries for collaboration; and the USA, China, England, Germany, France, Japan and Italy are in the top priority for collaboration, respectively. The results of collaboration pattern in highly cited papers indicated that the USA participates in more than half of collaboration patterns for producing highly cited papers.

AN EXAMINATION OF EXPERT SYSTEM NEURAL NETWORK AND DATA MINING FOR CARDIAC DISEASES

Now a day’s neural network are being used successfully in an increasing number of application areas. Expert system is used comprehensively in several research domains. Heart disease diagnosis is a complicated process and requires high level of expertise. This paper examines and aiming to study a expert system for diagnosing of heart disease using support vector machine, feed forward Back propagation technique, Radial Basis Function, Generalized Regression Neural Network etc with other Data mining techniques that are used for diagnosis for heart diseases. This paper studies the analysis and examines the different approaches used for the detection of heart disease using different neural network techniques and data mining techniques.

Neural Network Data Mining Clustering Optimization Algorithm

Data mining technology is an effective way to solve the problem of rich data and poor knowledge. Cluster analysis is an important content of data mining, including analysis ideas based on partition, hierarchy, density, grid and model. Neural network clustering is a typical clustering method based on model thinking. It is an organic combination of brain cognitive science and data mining. It has a strong theoretical connection with actual brain processing knowledge. This paper uses the K-means algorithm to optimize the neural network clustering data mining algorithm, and designs experiments to verify the neural network data mining clustering optimization algorithm proposed in this paper. The experimental research results in this paper show that on four UCI datasets, the mean MP values of the neural network data mining clustering optimization algorithms are 77.9 and 87.72, respectively, which are greater than the values of the other two algorithms. This paper also applies the algorithm to the study of the distribution of remaining oil. The algorithm has achieved obvious results in the cluster analysis of the degree of flooding.

Construction of online social network data mining model based on blockchain

Construction of online social network data mining model based on blockchain

Transfer learning based attack detection for wireless communication networks

AbstractWireless communication network attack detection is important in the field of wireless communication network data mining. Transfer learning is a powerful tool to solve the problem of link prediction in unlabeled wireless networks. In the feature mapping process of transfer learning, the loss of source domain information is very serious, which leads to the loss of knowledge in inverse mapping. To obtain more specific and complete information in the source domain, a link prediction method based on the distribution function fitting method of domain orthogonal term family is proposed. The key samples in the source domain are used to construct a family of orthogonal polynomials, and the least square approximation of the distribution function is obtained by using the family of orthogonal polynomials. The feature vector set of the key samples is extracted from the source domain, and the pseudo label is assigned to the target training set, so that the link classifier can complete the supervised link prediction task. In addition, an advanced performance of TLAD is obtained by comparing with other state‐of‐the‐art models in practice.

Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976

Dryland Food Security in Ethiopia: Current Status, Opportunities, and a Roadmap for the Future

Given the impact of COVID-19 and the desert locust plague, the Ethiopian food security issue has once again received widespread attention. Its food crisis requires comprehensive and systematic research to achieve the United Nations Sustainable Development Goal of zero hunger. This review discusses the current situation and the causes of food security in Ethiopia. We focus on the challenges in the food security assessment field. The article lists seven typical causes of food insecurity and three roots of food security in Ethiopia. Long-term food security assessment and a comprehensive understanding and manageability for food security causes are considered as the main existing research challenges. Climate-resilient management, water management, and long-term ecosystem network monitoring and data mining are suggested as potential roadmap for future research.

Components of Data Mining and Big Data Analytics in Intra-Data Center Networks

Data mining is considered as a vital procedure as it is used for locating brand-new, legitimate, useful as well as reasonable kinds of data. The assimilation of data mining methods in cloud computing gives a versatile and also scalable design that can be made use of for reliable mining of significant quantity of data from virtually incorporated data resources with the goal of creating beneficial information which is useful in decision making. The procedure of removing concealed, beneficial patterns, as well as useful info from big data is called big data analytics. This is done via using advanced analytics techniques on large data collections. This paper provides the information about big data analytics in intra-data center networks, components of data mining and also techniques of Data mining.

Investigation of the Mechanism of Traditional Chinese Medicines in Angiogenesis through Network Pharmacology and Data Mining.

Although traditional Chinese medicine is effective and safe for the treatment of angiogenesis, the in vivo intervention mechanism is diverse, complex, and largely unknown. Therefore, we aimed to explore the active ingredients of traditional Chinese medicine and their mechanisms of action against angiogenesis. Data on angiogenesis-related targets were collected from GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, DrugBank, and DisGeNET. These were matched to related molecular compounds and ingredients in the traditional Chinese medicine system pharmacology platform. The data were integrated and based on the condition of degree > 1, and relevant literature, target-compound, compound-medicine, and target-compound-medicine networks were constructed using Cytoscape. Molecular docking was used to predict the predominant binding combination of core targets and components. We obtained 79 targets for angiogenesis; 41 targets were matched to 3839 compounds, of which 110 compounds were selected owing to their high correlation with angiogenesis. Fifty-five combinations in the network were obtained by molecular docking, among which PTGS2-astragalin (−9.18 kcal/mol), KDR-astragalin (−7.94 kcal/mol), PTGS2-quercetin (−7.41 kcal/mol), and PTGS2-myricetin (−7.21 kcal/mol) were top. These results indicated that the selected potential core compounds have good binding activity with the core targets. Eighty new combinations were obtained from the network, and the top combinations based on affinity were KDR-beta-carotene (−10.13 kcal/mol), MMP9-beta-sitosterol (−8.04 kcal/mol), MMP9-astragalin (−7.82 kcal/mol), and MMP9-diosgenin (−7.51 kcal/mol). The core targets included PTGS2, KDR, VEGFA, and MMP9. The essential components identified were astragalin, kaempferol, myricetin, quercetin, and β-sitosterol. The crucial Chinese medicines identified included Polygoni Cuspidati Rhizoma et Radix, Morus alba Root Bark, and Forsythiae Fructus. By systematically analysing the ingredients of traditional Chinese medicine and their targets, it is possible to determine their potential mechanisms of action against pathological angiogenesis. Our study provides a basis for further research and the development of new therapeutics for angiogenesis.