Deep mining of elderly health data based on improved association clustering

Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process. Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning. Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images. The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.

The traditional data mining method of students’ physical health has some problems, such as low recall rate of data mining, long mining time, and poor mining accuracy. Therefore, this paper proposes a data mining method of college students’ physical health for physical education reform. Using association rules to construct the correspondence between the fitness test data, the fitness test data can be classified and the data training model can be built. The decision tree of data attribute was built, and the physical health data was segmented by the segmentation technology. The information entropy of health data was calculated by the decision tree, and the information gain of health data sample set was obtained. The C4.5 algorithm was used to improve the ID3 algorithm. The improved decision tree was used to obtain the physique data splitting attribute, and the information gain rate was obtained by the ID3 algorithm correction. The k -means algorithm is used to divide the data into clusters, according to which the physical health data mining of college students is realized. Experimental results show that the recall rate of the physical health data mining method proposed in this paper is as high as 96%, the data mining time is only 3 s, and the accuracy of data mining is as high as 98%, indicating that the method proposed in this paper can improve the physical health data mining effect.

The problems of generation of enterprise strategy in the context of the introduction of strategic management at domestic enterprises in conditions of limited resources are studied. Algorithms for the use of "data mining" and artificial neural networks for conducting strategic analysis and generating patterns of strategic alternatives for enterprise behavior in a dynamic business environment based on the principle of SWOT methodology are considered. A systematized task that can be solved using the "data mining" system and neural network methods of deep analysis ("BigData") in modern economic activity. The problem of using "data mining" to substantiate management decisions is considered. The peculiarities of using Business Intelligence technology for making business decisions, which is used by the international consulting company McKinsey, are analyzed. A model of substantiation of strategic decisions based on the use of artificial neural networks, principles of deep data analysis or data mining and corresponding discrete algorithms, methods and criteria for selecting factors of the external and internal environment of the enterprise is proposed.

BackgroundMedical postgraduates’ demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important.ObjectiveThis study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school.MethodsWe developed a medical data mining course called “Practical Techniques of Medical Data Mining” for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms—Rain Classroom, Tencent Meeting, and WeChat—were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience.ResultsIn total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students’ characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated “very positive” or “positive”), and MedHub received the best feedback, both in function (80/83, 96% chose “satisfied”) and teaching effect (80/83, 96% chose “satisfied”). The case study showed that our course was able to fill the gap between student expectations and learning outcomes.ConclusionsWe developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.

The impact of the air pollution phenomenon has been long studied, but most often with a fragmented approach, without closely looking at the relationship between different components that characterize it, such as sensor-based data, health data from institutional databases, and data on how it is perceived by human beings in social media. The research developed in this study introduces an integrated methodological framework that analyses sensor data on air pollution distributed in space and time, combined with health data and social data narratives that reflect how different communities perceive this phenomenon in space and time; exploring how these different heterogeneous sources can be combined to better understand the impact of air pollution phenomena at the large-city level in the Valley of Mexico. We introduce a Spatio-temporal data integration and mining framework that aims to discover trends and insights regarding the distribution of the impact of an air pollution phenomenon in terms of human health and perception. The main peculiarity of our methodological framework is the integration of different large data sources by combining a series of methods: NLP (topic modeling), data mining (data cubes, unsupervised learning, and clustering), and GIS capabilities (spatial interpolation, choropleth maps) that together provide a better understanding of the quantitative and qualitative patterns emerging at a different spatial scale and temporal granularity. Overall, this shows how social data, when combined with quantitative data, can provide a better understanding of the impact of a given phenomenon, such as air pollution.

Malware or malicious applications can cause catastrophic damage to not only computer systems but also data centers, web, and mobile applications from various industries; the Ministry of Interior, in particular, is the most important educational institution because they are more vulnerable to security breaches. Keeping stakeholder data safe from unwanted actors is a big concern that brings us to the concept of malware detection and prevention. Deep learning and data mining using artificial intelligence (AI) can be an efficient approach for developing anti-malware systems. Following suit, this study gave a thorough examination of malware detection methodologies and procedures. Initially, we attempted to provide a comprehensive description of malware, artificial intelligence, and data mining, as well as a listing of these technologies. The suggested system was described (whether this data is files, photographs, videos, or import limitations and is processed and identified by mining and deep learning data, and the system was trained on data). So far, our findings suggest that artificial intelligence and data mining can be used to construct anti-malware systems to detect and prevent malware assaults or security threats in software applications geared toward technological wonderland and its real-world application in the Ministry of Interior. To conclude, we outline dozens of possibilities for overcoming the observed restrictions and intend to expressly continue our efforts toward significant advancements in malware detection and prevention by implementing this proposal. We give a detailed look at the current ways to find malware, their flaws, and ways to make them more effective. We also explain how we're working on integrating the system. Our study shows that adopting future approaches to developing malware detection applications should provide significant advantages. Understanding this structure should help researchers do more research on malware detection and prevention using AI and data mining.

Due to the frequent use of the Internet and the generation of massive amounts of data, people have entered the era of big data. A large amount of data is generated in people’s daily life. Because these data contain a large amount of information and more complicated content, the process of data analysis and utilization must account for an increasing number of issues. As the problem becomes more complex, the use of deep learning in data mining becomes increasingly important. This article gives a certain introduction and understanding of deep learning and data mining and analyzes and summarizes the application of deep learning in data mining. It introduces a data mining approach for industrial and commercial companies, as well as the requirements of the classic CRISP-DM data mining method. It also exhibits the application of deep learning in industrial time-series data classification in the content of industrial and commercial companies.

Big Data is the most popular concept in this era, which is the massive amount of information and related technology generated by the information explosion in the era of “Internet+.” Big Data is the most popular concept of our time. With the most advanced technology to collect, analyze, organize, and store data, Big Data can effectively handle all kinds of complex information. Because of this, big data is widely favored by all walks of life. In China’s sports industry, the use of big data has become mature and has shown its unique advantages. With the development of campus soccer in China in the past decade, how to use big data to promote the sustainable development of campus soccer in China has become a key issue for sports workers to consider today. Based on the above background, this paper proposes a system combining data mining and personalized data recommendation to collect and analyze the information of campus soccer to promote the sustainable development of campus soccer. First, we propose a data mining method based on deep learning data mining network model combined with migration learning to address the data mining problem. The method uses the knowledge of historical model parameters and applies them to new tasks, thus solving the problem of network training when samples are lacking and improving data utilization and data mining effects. Then, for the data recommendation problem, a new deep learning method is proposed, which performs effective intelligent recommendation by pretraining. In the initial phase, the corresponding low-dimensional embedding vectors are learned, which capture information reflecting the relevance of students to soccer sports. During the prediction phase, a feed-forward neural network is used to model the interaction of student and soccer sport information, where the corresponding pretrained representative vectors are used as inputs to the neural network. Finally, it is experimentally verified that the data mining method proposed in this paper can effectively improve the data mining performance and efficiency, and the proposed data recommendation method possesses better accuracy than the traditional methods. The use of this system can effectively collect and analyze campus soccer information, which helps to develop campus soccer and promote the sustainable development of campus soccer.

The article explains the economic dynamics of the sports industry with adoption of deep learning algorithms and data mining methodology. Despite outstanding improvements in research of sports industry, a significant gap prevails with regard to proper quantification of economic benefits of this industry. Therefore, the current research is an attempt to filling this gap by proposing a specific economic model for the sports sector. This paper examines the data of sports industry covering the time span of 2012 to 2022 by using data mining technology for quantitative analyses. Deep learning algorithms and data mining techniques transform the gained information from sports industry databases into sophisticated economic models. The developed model then makes the efficient analysis of diverse datasets for underlying patterns and insights, crucial in realizing the economic trajectory of the industry. The findings of the study reveal the importance of sports industry for economic growth of China. Moreover, the application of deep learning algorithm highlights the importance of continuous learning and training on the economic data from the sports industry. It is, therefore, an entirely novel approach to build up an economic simulation framework using deep learning and data mining, tailored to the intricate dynamics of the sports industry.

We present HSDM, a full-day workshop on Health Search and Data Mining co-located with WSDM 2020's Health Day. This event builds on recent biomedical workshops in the NLP and ML communities but puts a clear emphasis on search and data mining (and their intersection) that is lacking in other venues. The program will include two keynote addresses by key opinion leaders in the clinical, search, and data mining domains. The technical program consists of 6 original research presentations. Finally, we will close with a panel discussion with keynote speakers, PC members, and the audience. This workshop aims to help consolidate the growing interest in biomedical applications of data-driven methods that becomes apparent all over the search and data mining spectrum, in WSDM's spirit of collaboration between industry and academia.

China Internet plus agriculture was first put forward in 2015 by the Chinese government's work report, laying the foundation for the development of Internet plus agriculture and promoting the rapid growth of e-commerce marketing of agricultural products. The combination of agricultural product marketing and e-commerce effectively reduces the intermediate links of agricultural product sales. Many e-commerce professional villages have sprung up in some rural areas across the country, and the number of rural e-commerce stores has continued to grow. At this stage, rural e-commerce has become a new way of agricultural trade, and rural e-commerce has formed a unique rural e-store. At present, the e-commerce market share of agricultural products in rural stores is very large, and its advantages are favored by the government, scientific research institutions, and agricultural products processing enterprises. However, with the gradual development of rural e-commerce, it has also encountered many difficulties. Based on this point, this study applies deep learning and data mining to optimize e-commerce marketing. First, with the growth of the online scale of agricultural product transaction data, the creation of traditional shallow model cannot meet the needs of online data processing. Therefore, this study decides to use the deep learning theory for optimization. It has excellent performance in the technical fields of big data processing and image and voice processing and has strong construction ability, which can effectively represent the characteristics of the model. Combined with the characteristics of e-commerce agricultural products processing and consumer practice, this study designs and develops a new customer value evaluation model based on data mining and e-commerce agricultural products value characteristics in the field of e-commerce. By combining deep learning and data mining technology, this study applies it to the field of e-commerce, so as to promote the transformation of marketing optimization.

In the era of mapreduce when “Internet +” is developed to “Big data x”, big data has gradually become a research focus closely followed by the scientific and technological circle, industry circle, and government departments. Big data analysis for moving taxi trajectory has gradually become a research hotspot in the fields of smart city information computing and smart city construction. At present, social problems such as traffic congestion, environmental degradation, and energy shortages are seriously affecting the safe and livable development of smart cities and their sustainable development. Through deep mining, analysis and comprehensive utilization of taxi trajectory data based on geographic location in the mobile social taxi network, it provides a new idea for the analysis of complex urban public transportation problems. This paper will focus on the new data analysis method and its practical application of deep analysis and mining of mobile taxi trajectory big data based on mapreduce. It’s dedicated to effectively solve the three major problems of data, including the real-time, robustness and accuracy, and provides theoretical basis and relevant practical technology for the application of urban dynamic monitoring and early warning control of complex urban public transportation network.

With the rapid development of information technology, the amount of all kinds of data information is increasing rapidly. As an important means to collect, store, and manage massive data, and then analyze and predict the habits and characteristics of certain groups of people and even the development trend of a certain industry, big data technology provides a comprehensive strategic basis for management decision makers that the traditional processing mode cannot match. Contemporary management accounting serves the whole process of enterprise internal control, so it will produce a large number of various data. With the explosive growth of network data and the increasing scale of database, more and more people begin to study data mining, and the classification algorithm, as the key technology in data mining, has also received extensive attention. In order to further improve the information technology level of enterprise management accounting and increase the depth of information application, many enterprises began to pay more attention to data mining, and through deep data mining, the depth and breadth of enterprise data analysis were improved. In the research of data and accounting informatization, data mining technology accounts for about 50% of informatization, which is the way for future development. With the advent of the information age, the dependence of enterprises on information technology in the process of accounting management has been further improved. If enterprises want to achieve better development in the information age, they need to pay more attention to the information technology of management accounting and improve the application ability of enterprise staff in information.

A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework

Purpose - This article aims to carry out a bibliometric analysis on data mining and occupational health and safety, covering the period between 2008 and 2020, for seven scientific databases and 68 articles.Theoretical framework - This study was theoretically based on concepts that involve data mining, machine learning and occupational health and safety.Design/methodology/approach - The selected articles were submitted to a statistical analysis, together with the evaluation of one of the bibliometric laws (Bradford's Law), comprising a number of citations, journals, authors, countries of origin, publication categories and an evaluation of production over the years.Findings - As a result, it was found that the most influential journal was Safety Science, and Taiwan was the leading country in terms of articles produced, with an average of 115 citations per article. The best-ranked journals related to Engineering and Health, both corresponding to 30% of the selected articles and journals.Originality/value - This study provides some insights into the growth of the data mining area together with occupational health and safety.Keywords - Bibliometrics analysis. Occupational health and safety. Data mining.