Combination Of Big Data Research Articles

Research on the Application of Big Data and Artificial Intelligence in Search Engine

With the development of the Internet, search engine as an important tool of information retrieval, more and more need to deal with large-scale, complex data. The combination of big data and artificial intelligence technology provides a new solution for search engine optimization. This paper mainly discusses the application of big data and artificial intelligence in search engine, including data collection and processing, data mining and pattern recognition, user behavior analysis, personalized recommendation algorithm, machine learning algorithm, deep learning algorithm, reinforcement learning algorithm and the intelligence of personalized recommendation system.

Intelligent Nursing System for the Elderly Based on Big Data

The current problem of providing for the aged in China is extremely serious. As a weak member of society, the basic right of the elderly is the harmonious and stable development of society, especially in China, where the problem of aging is extremely severe, and the general pension institutions cannot fully and meticulously meet the precise needs of different elderly people. Therefore, this paper mainly studies the intelligent nursing system for the elderly based on big data, aiming to meet the requirements of the elderly of different ages for the elderly through the combination of big data and old-age care. This old-age care model is based on the usual application environment, the internet “big data” as the foundation, accurately applying intelligent technology to provide nursing services for the elderly, and providing interconnected, intelligent, convenient, and efficient old-age care services for the elderly, thus building a real-time, safe, convenient, and low-cost old-age care mechanism.

Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

AbstractIn recent years, with the rapid development of the Internet of Things and communication technology, the application of network multimedia has become increasingly popular. At present, network multimedia has become a new type of mass media. Due to its interchangeability and extensive dissemination, it creates favourable conditions for information dissemination and acceptance in terms of vision, hearing, touch etc. However, at the same time, there are also some problems. The network multimedia does not have enough original information and the information retrieval is not intelligent, so this has caused great trouble to users. In order to solve these problems, the authors attempted to use the combination of Big Data (BD) and virtual Artificial Intelligence (AI) technology to realise the intelligent design of network multimedia. Therefore, based on the theoretical analysis, the authors also verified the effect of BD and virtual AI technology on the intelligent design of network multimedia. It was found that the intelligent design method proposed by the authors had good application effect in network multimedia, and could effectively improve the information retrieval speed of network multimedia. The query time of this method was 1.87 s less than that of the traditional method when the amount of information was 1000 and 18.16 s less than that of the traditional method when the amount of information was 40,000. At the same time, this method could also improve the accuracy of network multimedia information recommendation, and make network multimedia have better sharing, so as to provide more services for users. In addition, the discussion of BD and virtual AI technology in network multimedia intelligent design could also broaden the application scope of the Internet of Things and promote its better development.

Role of Sports Based on Big Data Analysis in Promoting the Physique and Health of Children and Adolescents

ABSTRACT Healthy body is the foundation of young people's growth. With the popularization and globalization of the Internet, multimedia technology is rapidly changing the impact of traditional media on the growth of young people. The current health situation of young people is not optimistic. The decline in physical fitness, obesity and psychological dysplasia of adolescents have aroused the concern of all sectors of society. In recent years, the emergence and dissemination of big data (BD) has brought a new dimension to the value of data applications. The combination of BD and youth health services provides young people with good health opportunities. Through the recording, analysis and release of adolescent physical health data, the system has established an extensive knowledge database on adolescent physical and mental health, thus improving the physical health of adolescents. This paper summarized and combed the overview and application of BD, and analyzed and discussed the reasons for the continuous decline of young people's physique. Through the analysis of the application of BD in the promotion of young people's physical health, this paper proposed more achievable improvement strategies and plans, and then summarizes and discusses the experiment. According to the survey and experiment, the random simulation algorithm was introduced into daily exercise, diet and life preference. The new system and health improvement strategy designed for teenagers’ physical health using BD could help students improve their physical health by 55%.

Reform of college students’ teaching management informatization under the background of big data and IoT

With the continuous layout of intelligent processing technology in the teaching field, it has become an important trend to apply big data and Internet of Things technology to the teaching management of college students. Big data technology can comprehensively analyze the teaching situation of students through massive data, and then provide the best solution, the current big data technology already has a strong technical foundation, which can provide assistance for students’ teaching. The Internet of Things technology can collect personal information of students through sensors and miniature portable devices, and provide the data to the background server for big data analysis. In order to analyze the current status of the informationization of teaching management for college students, this paper first surveys some colleges and institutions through questionnaire surveys, and conducts data analysis on the collected questionnaires, and puts forward a solution based on big data and the Internet of Things based on the data analysis results. Finally, the combination of big data and Internet of Things technology for student teaching data collection, effect evaluation scheme, intelligent arrangement of integrated courses, intelligent recommendation scheme for students’ teaching needs, and teaching management information visualization scheme are analyzed, and it is found that the combination of big data and Internet of Things related technologies It can effectively improve the efficiency of teaching management of college students.

Takaful Industry in the Era of Technological Advancement

The aim of this paper is to investigate how the takaful industry can address the issue of technological advancement. Furthermore, to explore the way by which takaful operators can compete with fintech insurers as well as in which areas takaful operators should focus on to innovate, e.g., technology or products, Additionally, to find out if there are any shariah concerns if takaful operators jump on the bandwagon of these fintech insurers, The findings of the research show that the impact of technological innovations extends beyond the takaful value chain itself to the whole takaful eco-system. The combination of big data and advanced analytics is reshaping the takaful industry, from underwriting to claims and marketing, by inventing new products and distribution channels. The threat from big-tech companies such as Google, Facebook, Apple, and Amazon have broadened the scope of the operator’s problem because these big-tech companies are currently offering insurance products through their websites. The Takaful industry should tie up with big tech companies to sell their products through their websites. Since these companies have deep knowledge and experience in technology, they have also gained a decent reputation as they have proven records of providing excellent services to their customers and have a vast global reach. This research brings insights for policymakers, regulators, and practitioners. The main insights in this paper are technological advancements in the takaful industry. It further answers the question of which areas Takaful operators should focus on to innovate, e.g., technology or products.

Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database

Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, can produce a large amount of data at high speeds and volumes that must be stored for real-time or post-processing and analysis. In addition to sensor data, metadata is an important component and can vary between biomechanical applications. Currently however, each application typically has its own unique data flow and storage solution. In this research, we present a universal data model solution that can be applied to any sensor-based biomechanical application in sport and physical rehabilitation. Our proposed cloud platform architecture allows for the manipulation of sensor data and metadata using a combination of Big Data and conventional techniques. The main idea of this research is to develop a platform that allows a universal way for any biomechanical application to handle its data regardless of the type of data and metadata. This is achieved by creating a universal data model, and implementing this data model in a generalized architecture using a graph database. We demonstrate the benefits of this approach using examples from existing biomechanical systems and describe the development of the cloud platform architecture and the underlying data model. We also provide an example of the use of this platform in a sport shooting application. This approach is unique in that it allows data from different sources and applications to be stored and processed using the same procedures and techniques, facilitating data analysis and application development. We envision this system will expand to multiple different biomechanical applications in the future. We expect that in time, the ability to compare various data and store different biomechanical datasets will become necessity. With the advantages of modern recommender systems and utilization of artificial intelligence, huge amounts of relevant and well-prepared data with useful metadata are required thus having such system is an important advantage for future biomechanical systems development. With the increase of people’s awareness and usage of devices that increase well-being and quality of life, presented platform and similar systems will play a pivotal role in shaping the future lifestyle.

Complete Covid-19 Vaccination and Room Occupation Rates Nexus Tourism in East Java

The purpose of this study is to examine the effect of complete vaccination up to the second dose on the mobility of the population to recreational and retail areas that describe tourism activities, to examine the effect of population mobility to recreational and retail areas on the occupancy rate of hotel rooms and to examine the factors that influence the level of high and low the rating value of a hotel or other accommodation service that describes the level of satisfaction with hotel services in East Java. Inferential statistical methods used are time series analysis with ARIMAX, Pearson Correlation Analysis and Ridge Regression Analysis. The data used is a combination of Big Data and Official Statistics. Big Data is obtained from scrapping results in Google Mobility Index (GMI), the Ministry of Health of the Republic of Indonesia, and www.tiket.com as well as official statistics sourced from the Central Boards of Statistics Indonesia (BPS). Based on the results of ARIMAX, it can be concluded that complete vaccination up to the second dose affects the mobility of the population to recreational and retail areas that indicate tourism activities, then based on the results of the Pearson correlation, it can be concluded that there is a strong and positive correlation between the mobility of residents to recreational areas and room occupancy rates hotels in East Java and based on the Ridge Regression analysis, it can be concluded that there is a positive influence of price appropriateness, cleanliness, service, facilities, and location on consumer ratings that describe the level of satisfaction with hotel services in East Java and the dimension that has the most influence on the level of hotel satisfaction in East Java is price appropriateness

Exploration on English Teaching Reform under the Combination of Big Data and Artificial Intelligence Technology

Exploration on English Teaching Reform under the Combination of Big Data and Artificial Intelligence Technology

Analysis of different machine learning classifiers on MP election commission and breast cancer big dataset

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naïve Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

Approximate Methods for Bayesian Computation

Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the impetus given by the successful application of Markov chain Monte Carlo (MCMC) sampling, the combination of big data and complex models conspire to produce significant challenges for the traditional MCMC algorithms. We review modern algorithmic developments addressing the latter and compare their performance using numerical experiments.

Digital Media Teaching and Effectiveness Evaluation Integrating Big Data and Artificial Intelligence.

With the development of digital media technology, its application in teaching and learning is becoming more widespread. Digital media technology helps present information in transmitting knowledge or skills, reduces cognitive load, and promotes understanding of knowledge. Evaluation of the effectiveness of digital media teaching has also become important. A scientific and reasonable evaluation of digital media teaching effectiveness can help teachers select digital media technology and grasp the amount, degree, and timing of digital media use to change teaching effectiveness. This paper proposed using a combination of big data and artificial intelligence methods to evaluate the effectiveness of digital media teaching methods using the RBF neural network model. The digital media teaching effectiveness evaluation was used as the input variable of RBF, the degree of digital media effectiveness was the output variable and the neural network was trained through the collected sample data. The research results showed that the RBF neural network model proposed in this paper has a strong generalization and extension ability in evaluating digital media teaching effectiveness, providing a new way to evaluate digital media teaching effectiveness.

Research on University Financial Accounting Management System Based on Big Data and Blockchain Data Fusion

The majority of traditional finance management in colleges and universities is manual. This backward management mode brings a lot of inconvenience to financial processing. An essential concern in the daily financial administration of colleges and universities is how to efficiently gather, handle, and evaluate this important financial information and apply this beneficial knowledge to the daily management of colleges and universities. College and university finance administration has become increasingly complex due to the rapid growth of these institutions. The standard financial accounting management system is far from adequate for the daily needs of colleges and universities as their size grows. As a result, a new financial accounting management system for universities is being developed based on the combination of big data and blockchain data. The embedded processor, DDR2 memory chip, network interface, and USB interface are all designed by the hardware section. The software element examines the needs of university financial accounting management before designing a model based on big data and blockchain integration for university financial accounting management. Finally, the financial accounting management function module and database are designed and tested. The experimental results show that the designed financial accounting management system can effectively carry out financial management, has good performance, and has certain application value.

Challenges and Optimization Paths of Guzheng Professional Education in Colleges under Big Data Era.

As a treasure among Chinese national musical instruments, guzheng is an important part of traditional Chinese music. As the art of national music goes to the world, the art of guzheng has been widely promoted. As the best form to carry forward the art of guzheng, the teaching of guzheng majors in colleges is significant in teaching and continuously improves guzheng art accomplishment. Oral teaching and step-by-step music theory and technique teaching are typical ways of teaching musical instrument performance in colleges. However, under big data, Chinese education is undergoing a profound change, and the combination of big data and education has become a new contemporary education method. This work studies the guzheng professional education in colleges under big data. First, this work aims at the existing outstanding issues of guzheng teaching in colleges and studies the challenges and optimization paths of guzheng professional education in colleges under big data. Second, this work proposes a multiscale residual attention fusion network (MSRAFNET) to evaluate the teaching quality of guzheng majors in colleges in the era of big data. The feature extraction of the network model is mainly completed by the residual module, which is composed of several multiscale residual learning units. Adding an attention mechanism to the multiscale residual learning unit can enhance the feature extraction of key information by the network and reduce the interference of redundant information, which is more conducive to the learning of data features. It adopts the design of GAP and Dropout to reduce spatial parameters in network training, and the effect of antioverfitting is better. Third, this work systematically evaluates the optimization path of Guzheng education and MSRAFNET, and the systematic experiments verify the superiority of the designed method.

Big data in basic and translational cancer research.

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of ‘big data’ in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

George Floyd and cosmopolitan memory formation in online networks: A report from Northern Europe

This work analyses the presence and management As personalized digital networks have increased in cultural and political relevance, there is a more urgent need to understand their role in democratic memory-formation. Moreover, scholars have suggested that, in a globalized digitalized age, collective memory could extend to transnational publics. This study aims to advance the understanding of memory on global social networks by investigating the way the death of George Floyd in the summer of 2020 was treated and understood by Twitter-users outside the United States. Using a combination of big data and contemporaneous qualitative interviews with users in Norway, Sweden, and Denmark, the paper brings the concept of cosmopolitan memory into the social media era. The study finds that users fused the event and its aftermath with observations of injustice in their own countries. However, this process operated differently among users of different ideological outlooks. Another key finding is that users on the radical right resented the uptake of the event as a cosmopolitan memory, and employed techniques termed as “combative counter-memory”.

Artificial Intelligence in Digital Health: Issues and Dimensions of Ethical Concerns

Artificial intelligence (AI) is transforming the healthcare system at a breakneck pace by improving digital healthcare services, research, and performance, fueled by the combination of big data and strong machine learning algorithms. As a result, AI applications are being employed in digital healthcare domains of which some where previously regarded as only done by human expertise. However, despite AI's benefits in digital healthcare services, issues and ethical concerns need to be addressed. Using mapping review methodology, a taxonomy of issues and ethical concerns surrounding the employment of AI in healthcare is presented and discussed. Moreover, policy recommendations and future research directions are presented.

Characteristics of COVID-19 and Research Progresses on Genetic Engineering Vaccine Based on Big Data.

Big data platforms can effectively analyze the data and maximize the value of the data by mining the text, digital, video, and image data in various industries. The combination of big data and various industries has brought great changes to the development of the industry. Providing data according to demand can save more time and promote the development of the industry. SARS-CoV-2 (COVID-19) is sweeping across the world, and it has spread to several countries and regions. Human infections have been reported all around the world. Due to the unique characteristics of COVID-19, no specific medicine is available yet to cure patients before the successful research and development of vaccines. Hence, it is of important significance to research and develop vaccines. Guided by the biological characteristics of COVID-19 and the philosophy of synthetic biology, this study reviews the developed genetic engineering vaccines.

Application of Artificial Intelligence in Cardiovascular Imaging.

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems in various industries has continued to expand, resulting in a huge increase in industry data. As for the medical industry, huge unstructured data has been accumulated, so exploring how to use medical image data more effectively to efficiently complete diagnosis has an important practical impact. For a long time, China has been striving to promote the process of medical informatization, and the combination of big data and artificial intelligence and other advanced technologies in the medical field has become a hot industry and a new development trend. This paper focuses on cardiovascular diseases and uses relevant deep learning methods to realize automatic analysis and diagnosis of medical images and verify the feasibility of AI-assisted medical treatment. We have tried to achieve a complete diagnosis of cardiovascular medical imaging and localize the vulnerable lesion area. (1) We tested the classical object based on a convolutional neural network and experiment, explored the region segmentation algorithm, and showed its application scenarios in the field of medical imaging. (2) According to the data and task characteristics, we built a network model containing classification nodes and regression nodes. After the multitask joint drill, the effect of diagnosis and detection was also enhanced. In this paper, a weighted loss function mechanism is used to improve the imbalance of data between classes in medical image analysis, and the effect of the model is enhanced. (3) In the actual medical process, many medical images have the label information of high-level categories but lack the label information of low-level lesions. The proposed system exposes the possibility of lesion localization under weakly supervised conditions by taking cardiovascular imaging data to resolve these issues. Experimental results have verified that the proposed deep learning-enabled model has the capacity to resolve the aforementioned issues with minimum possible changes in the underlined infrastructure.

A Deep Learning Approach to Design and Discover Sustainable Cementitious Binders: Strategies to Learn From Small Databases and Develop Closed-form Analytical Models

To reduce the energy-intensity and carbon footprint of Portland cement (PC), the prevailing practice embraced by concrete technologists is to partially replace the PC in concrete with supplementary cementitious materials [SCMs: geological materials (e.g., limestone); industrial by-products (e.g., fly ash); and processed materials (e.g., calcined clay)]. Chemistry and content of the SCM profoundly affect PC hydration kinetics; which, in turn, dictates the evolutions of microstructure and properties of the [PC + SCM] binder. Owing to the substantial diversity in SCMs’ compositions–plus the massive combinatorial spaces, and the highly nonlinear and mutually-interacting processes that arise from SCM-PC interactions–state-of-the-art computational models are unable to produce a priori predictions of hydration kinetics or properties of [PC + SCM] binders. In the past 2 decades, the combination of Big data and machine learning (ML)—commonly referred to as the fourth paradigm of science–has emerged as a promising approach to learn composition-property correlations in materials (e.g., concrete), and capitalize on such learnings to produce a priori predictions of properties of materials with new compositions. Notwithstanding these merits, widespread use of ML models is hindered because they: 1) Require Big data to learn composition-property correlations, and, in general, large databases for concrete are not publicly available; and 2) Function as black-boxes, thus providing little-to-no insights into the materials laws like theory-based analytical models do. This study presents a deep learning (DL) model capable of producing a priori, high-fidelity predictions of composition- and time-dependent hydration kinetics and phase assemblage development in [PC + SCM] pastes. The DL is coupled with: 1) A fast Fourier transformation algorithm that reduces the dimensionality of training datasets (e.g., kinetic datasets), thus allowing the model to learn intrinsic composition-property correlations from a small database; and 2) A thermodynamic model that constrains the model, thus ensuring that predictions do not violate fundamental materials laws. The training and outcomes of the DL are ultimately leveraged to develop a simple, easy-to-use, closed-form analytical model capable of predicting hydration kinetics and phase assemblage development in [PC + SCM] pastes, using their initial composition and mixture design as inputs.