Internet Data Mining Research Articles

Research on Financial Risk Monitoring and Early Warning in China Based on Big Data Analysis

With the continuous development of China's economy, the financial industry plays a more and more important role in the national economy. At the same time, the financial risk(FR) are increasing and becoming increasingly complex. While sharing the huge benefits brought by financial globalization and financial deepening, China is also faced with greater FR from home and abroad, which greatly increase the possibility of financial crisis in China. In order to enhance the initiative of risk management and ensure the smooth operation of asset quality, we must rely on a powerful system tool, make use of the international advanced risk early warning management concept, and use Internet thinking and data mining technology to dig deep into hidden risk factors, discover hidden risks in advance and deal with them in time, so as to ensure the automatic monitoring ability of credit business. Based on the theoretical research on the generation, accumulation and diffusion of systematic FR, this paper explores the introduction of big data methods into the research on systematic FR in China, comprehensively uses traditional and emerging indicators and models in big data network analysis methods, analyzes the contagion effects of regional systematic FR from multiple perspectives and levels, improves research methods, and enriches research perspectives. The introduction of SIRS model on scale-free network provides a new powerful tool for the study of regional systemic FR.

Data Mining in Internet of Things Systems: A Literature Review

Data Mining in Internet of Things Systems: A Literature Review

A systematic survey of data mining and big data analysis in internet of things

A systematic survey of data mining and big data analysis in internet of things

Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things

This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

Kansei engineering for new energy vehicle exterior design: An internet big data mining approach

New energy vehicles (NEVs) such as electronic cars represent a major trend in the automobile industry, where most their exterior designs still follow those of convention fuelled vehicles (FVs). It is important to investigate whether NEV users have unique requirements that differ from those of traditional users. Kansei engineering is a practical tool for perceptual demand analysis. However, the conventional method requires questionnaires or surveys to perform limited data collection. In this study, we utilised massive internet data to collect user Kansei requirements for NEV exterior design. The Scrapy crawler was adopted for data collection and a bidirectional long short-term memory, conditional random field, and multilayer perceptron framework was developed for text mining. To quantify design features and Kansei image scores, a hybrid Apriori + structural equation model (SEM) system is proposed, where the data-driven Apriori algorithm can explore the hidden relationships in big user generated comments, while the SEM model captures the users’ behaviour and decision procedure so that to provide interpretable results. In addition, the association rules mined from user comments by Apriori can facilitate the specification of a complicated SEM model, substantially reducing the modelling and calibration effort. Goodness-of-fit results suggest that the proposed model outperforms conventional models. A case study on 1805 automobiles, 287 brands, and 369105 comments was conducted and the results suggest that some design features that would increase the Kansei image scores for conventional FVs may have the opposite effect on NEVs. Discussions on engineering and managerial insights are presented and the discovered rules and relationships are employed to develop a design-aided system.

What are you doing here? Investigating on an unexpected association in shallow Mediterranean dark caves sheds light on the diet of Marionia blainvillea (Mollusca, Gastropoda, Nudibranchia)

In a shallow dark cave along the coast of Capo Palinuro (Campania, Italy), the finding of an unusual aggregation of individuals belonging to Marionia blainvillea (Gastropoda, Nudibranchia) has allowed to identify a population of Cervera atlantica (Cnidaria, Anthozoa, Alcyonacea) and to add this latter to the list of the hosts preyed by this tritoniid nudibranch. This association was confirmed by a second observation in a dark shallow cave in the Island of San Pietro (Sardinia, Italy). Moreover, a subsequent internet data mining shed light on the diet of M. blainvillea adding useful ecological notes on the scarcely known trophism characterizing Marionia species. Cervera atlantica is reported for the first time along the continental coast of Italy.

Predicting agricultural and livestock products purchases using the Internet search index and data mining techniques

PurposeThis study identifies whether the Internet search index can be used as effective enough data to identify agricultural and livestock product demand and compare the accuracy of the prediction of major agricultural and livestock products purchases between these prediction models using artificial neural network, linear regression and a decision tree.Design/methodology/approachArtificial neural network, linear regression and decision tree algorithms were used in this study to compare the accuracy of the prediction of major agricultural and livestock products purchases. The analysis data were studied using 10-fold cross validation.FindingsFirst, the importance of the Internet search index among the 20 explanatory variables was found to be high for most items, so the Internet search index can be used as a variable to explain agricultural and livestock products purchases. Second, as a result of comparing the accuracy of the prediction of six agricultural and livestock purchases using three models, beef was the most predictable, followed by radishes, chicken, Chinese cabbage, garlic and dried peppers, and by model, a decision tree shows the highest accuracy of prediction, followed by linear regression and an artificial neural network.Originality/valueThis study is meaningful in that it analyzes the purchase of agricultural and livestock products using data from actual consumers' purchases of agricultural and livestock products. In addition, the use of data mining techniques and Internet search index in the analysis of agricultural and livestock purchases contributes to improving the accuracy and efficiency of agricultural and livestock purchase predictions.

Application of Artificial Intelligence Techniques in Operating Mode of Professors’ Academic Governance in American Research Universities

Artificial intelligence technology is an important transformative force for teaching innovation in the intelligent era. It is being widely used in American school teaching, including the design of intelligent tutoring systems to achieve precise problem solving, the machine learning technology to ensure personalized activity design, the creation of intelligent virtual reality to promote classroom teaching contextualization, and the development of intelligent evaluation systems to ensure the scientific evaluation of capabilities. In the process of advancing the teaching and application of artificial intelligence technology, the United States has built a linkage mechanism of federal leadership, university follow‐up, and social collaboration and implemented the smart technology in school teaching and professors’ academic governance. This paper is aimed at studying the professors’ academic governance of American research universities by Internet data mining, historical analysis method, documentary method, survey method, and other methods. Professors’ academic governance is a vital part of the modern university system that causes the institutional reform of the internal governance structure of modern universities. The United States is a powerful country in higher education, and professors in American research universities have always participated in university academic governance for centuries. By studying the definition, history, and development and mode of operation of professors’ academic governance in American research universities, the results indicate a clear division of power and responsibility between the professors and administrators based on an artificial intelligence decision system in American research universities. Also, there is a good communication platform based on artificial intelligence environment for professors to discuss their opinions on academic affairs. Third, professors exercise academic power under the guarantee of diversified guaranteed systems based on the artificial intelligence evaluation system and the ideology of mutual respect based on the artificial intelligence management and service system. Studying the application of artificial intelligence techniques in operating mode and enlightenment of professors’ academic governance in an American research university is of great significance to promote the construction of other modern universities’ professors’ academic governance system.

Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules

As the big data, Internet of Things, cloud computing, and other ideas and technologies are integrated into social life, the big data technology can improve the corporate financial data processing. At the same time, with the fiercer competition between enterprises, investors and enterprises have paid more attention to the role of financial crisis warning in corporate management. The work selected the multiple financial indicators based on big data mining in Internet of Things. The rules between all financial indicators were found to choose more representative financial risk indicators. Then the frequent fuzzy option set was determined by FCM (fuzzy cluster method), parallel rules, and parallel mining algorithm, thus obtaining the fuzzy association rules that satisfy the minimum fuzzy credibility. Finally, the relevant data of listed companies were selected to analyze the corporate financial risks, which verified the method proposed in the work.

Research on Risk Prevention System of Judicial Administration Based on Big Data Analysis

With the further development of the Internet, big data and deep data mining technology is quietly changing all aspects of social life with its unique functional characteristics. However, affected by many factors at home and abroad, China’s judicial administration is facing serious internal and external crisis. There are many potential risks in the process of judicial administration. If we can rely on modern science and technology to establish an effective risk prevention system, it will effectively protect the judicial administration according to law. Among various emerging technical means, big data technology provides an effective way for the establishment of risk prevention system with its universality, accuracy and predictability. Based on big data technology, this paper builds a risk prevention system in the field of judicial administration. Through the outstanding achievements of the government in risk prevention in recent years, the results show that good risk prevention effect has been achieved in the aspects of administrative efficiency, anti-corruption, public opinion guidance and so on.

Design of cancer classification and visualization platform based on Internet big data

With the development of Internet technology, Internet technology is widely used in the medical industry. Based on the medical Internet big data, using visualization, data mining and other technologies, combined with distributed crawler, HTTP data transmission technology, vue2 front-end framework and the application of visualization technology, this paper completed the development and design of the analysis platform based on the internet medical big data. From the three aspects of presentation layer, business logic layer and data access layer, the architecture can show the number of hospitals, the proportion of doctors, the number of departments, the hospital’s praise, poor evaluation and other information. Then, the cancer big data classification model based on the new bionic optimization algorithm is established. The experimental results show that the method has high classification accuracy and speed. In the era of big data, the integration of early diagnosis and treatment of cancer resources and information construction is very necessary and has far-reaching significance.

Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

An efficient scheme for secure feature location using data fusion and data mining in internet of things environment

SummaryFeature location (FL) is performed to find the relationships between domain concepts and other software artifacts. One major problem in maintaining a software system is to understand how many functional features exist in a system and how these features are implemented. Also, poor security is the prime problem in the FL system. However, the existing recent FL techniques use a textual and dynamic approach, which is not found to be secure, keeping in view the changes in the description of security attacks. To overcome this drawback, this work proposed a novel secure approach for FL utilizing data fusion as well as data mining for the internet of things environment. Firstly, the repeated test cases (TC) are eradicated as of the labeled TC. Next, important attributes are selected using the artificial flora optimization algorithm from the removed labeled TC. Then, association rule mining is performed to ascertain closed attributes. Subsequently, encrypt the closed attributes utilizing Caesar Cipher‐Rivest, Shamirs, as well as Adelman algorithm. After that, the score value of the closed attributes counts was found utilizing entropy calculation. Finally, the score value is given as input to the normalized‐K‐Means (N‐[K‐Means]) algorithm, where the score value is normalized utilizing min‐max normalization and then grouped utilizing K‐Means algorithm (KMA). It proffers better results for FL in the source code. The proposed N‐(K‐Means) performance is found better in comparison to the KMA and latent semantic indexing methods. The proposed system proffered better FL results in comparison to the other prevailing methods.

Internet public informatioan text data mining and intelligence influence analysis for user intent understanding

Internet public informatioan text data mining and intelligence influence analysis for user intent understanding

The construction of internet data mining model based on cloud computing

The construction of internet data mining model based on cloud computing

Artificial Intelligence in Smart Health: Investigation of Theory and Practice

The World Health Organization defines Smart Healthcare as "Information and Communication Technology applications in the medical and health fields, including medical care, disease management, public health monitoring, education, and research." In addition, many scholars believe that "Smart Healthcare" refers also to the integration of medical informatics, public health, and business applications mainly through the Internet and related artificial intelligence and data mining technologies in order to provide more accurate personal healthcare services and health information. The concept of deep learning has gained ground rapidly in recent years. While deep learning is usually applied to the studies of image/object recognition such as board game notations, paintings, people/things/objects in pictures, and so on, it is also often applied to the extraction of features. However, researchers have rarely used deep learning methods to predict outcomes in the medical and healthcare fields, preferring instead to make these predictions using algorithms based in traditional statistical methods and regression analysis. This paper introduces and investigates deep learning methods in the context of predicting outcomes in the medical and healthcare fields.

The geometric blueprint of perovskites.

Perovskite minerals form an essential component of the Earth's mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the "no-rattling" principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.

Systematic survey of big data and data mining in internet of things

In recent years, the Internet of Things (IoT) has emerged as a new opportunity. Thus, all devices such as smartphones, transportation facilities, public services, and home appliances are used as data creator devices. All the electronic devices around us help our daily life. Devices such as wrist watches, emergency alarms, and garage doors and home appliances such as refrigerators, microwaves, air conditioning, and water heaters are connected to an IoT network and controlled remotely. Methods such as big data and data mining can be used to improve the efficiency of IoT and storage challenges of a large data volume and the transmission, analysis, and processing of the data volume on the IoT. The aim of this study is to investigate the research done on IoT using big data as well as data mining methods to identify subjects that must be emphasized more in current and future research paths. This article tries to achieve the goal by following the conference and journal articles published on IoT-big data and also IoT-data mining areas between 2010 and August 2017. In order to examine these articles, the combination of Systematic Mapping and literature review was used to create an intended review article. In this research, 44 articles were studied. These articles are divided into three categories: Architecture & Platform, framework, and application. In this research, a summary of the methods used in the area of IoT-big data and IoT-data mining is presented in three categories to provide a starting point for researchers in the future.

What Can Google Inform Us about People's Interests regarding Dental Caries in Different Populations?

The diagnosis or suspicion of dental caries can lead people to seek additional information on the Internet through the use of structured queries in search engine tools. This action generates a considerable volume of data, which can be analyzed to provide a better understanding of the public's behavior linked to the consumption of oral health information. This study aimed to assess the volume and profile of web searches on dental caries-related queries performed by Google users from different countries. The monthly variation of the Search Volume Index (SVI) for dental caries was obtained in Google Trends for the period between January 2004 and September 2016. The validity of SVI data was assessed by their levels of stability and correlation with the disability-adjusted life-years (DALYs) for permanent teeth. In all countries, a trend of an increasing interest of Google users in dental caries issues was revealed by the comparison of the means observed in the predictive models and those in the last 12 months. The interest levels varied throughout the year, with the observation of the highest SVI values in the spring and the lowest in the summer. The most popular queries were markedly associated with symptoms and treatments, with a little interest in prevention. In conclusion, the use of Internet data mining could be helpful in establishing the dental needs of specific population groups in a near real-time, since the web consumption of dental information is increasing in importance and appears to have a direct relation with untreated dental caries.

A new algorithm of stock data mining in Internet of Multimedia Things

The explosive growth in the number of enterprises of Internet of Multimedia Things stocks reflects the economic level of an area. Internet of Multimedia Things has emerged as a new research concept in the world, in which a new data mining algorithm is proposed to study enterprise operation ability of Internet of Multimedia Things. Firstly, an image processing method based on wavelet transform is proposed. We do a big data analysis of the stock’s historical data to obtain its plate leading stock average data and volatility of individual stocks data image. The number of the singular value points of the individual stocks was obtained according to their fluctuation, and we could get the singular rate from the numbers of singular values points. Stock’s stability could be judged comprehensively by comparison between the trend of fluctuations in individual stocks and leading stock, singular value point, and singular rate. The results of this paper could help investors to grasp the volatility of the stock market, making predictive analysis, and getting a higher return. The experimental results show that our approach has better ability to mine the essences in Internet of Multimedia Things. Data mining also can get the development level of Internet of things enterprises.