Data Analysis Technology Research Articles

Environmental Resilience Technology: Sustainable Solutions Using Value-Added Analytics in a Changing World

Global climate change and associated environmental extremes present a pressing need to understand and predict social–environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we present a research-to-commercialization (R2C) model and conduct a case study using it to address the wicked wildfire problem through an industry–university partnership. We systematically evaluated 39 different user stories across eight user personas and identified information gaps in public perception and dynamic risk. We discuss utility and challenges in deploying such a model as well as the relevance of the findings from this use case. We find that research-to-commercialization is non-trivial and that academic–industry partnerships can facilitate this process provided there is a clear delineation of (i) intellectual property rights; (ii) technical deliverables that help overcome cultural differences in working styles and reward systems; and (iii) a method to both satisfy open science and protect proprietary information and strategy. The R2C model presented provides a basis for directing solutions-oriented science in support of value-added analytics that can inform a more resilient future.

Retracted: Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Retracted: Evaluation Method of Advanced Mathematics Teaching Reform Effect Based on Big Data Analysis Technology

Retracted: Evaluation Method of Comprehensive Quality of Environmental Protection Teachers Based on Big Data Analysis Technology.

[This retracts the article DOI: 10.1155/2022/6848352.].

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.

Machine learning (ML) is considered an important technology for future data analysis in health care. The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..

To Categorize and Forecast the Type of crime and Person-identification using Machine Learning

Crimes are an irritant to society and have a discernible effect on it. Many law enforcement organizations currently have large amounts of crime- related data that need to be processed in order to become useful information. Crime data are complex since they include a lot of various formats and features. Law enforcement organizations face a number of challenges in their efforts to prevent crimes. To assist law enforcement agencies in conducting descriptive, predictive, and prescriptive analysis on crime data, we propose a desktop application for crime data analysis. Because the application was modular, each component was built separately. Person identification makes use of data analysis and biometric technologies to accurately identify individuals. Databases are able to record and store these characteristics. Keywords-Crime, Prediction, Person- Identification, SVM, Haar-cascade algorithm

Research on Intelligent Operation and Maintenance (O＆M) Method of Complex Products based on Digital Twin

<p>With the development of advanced data analysis technology, the manufacturing industry is moving towards the direction of intelligence. To solve the problems of low operation and maintenance (O&M) efficiency, passive O&M personnel and low intelligence of complex products, a digital twin-based intelligent O&M method for complex products is proposed. A digital twin intelligent O&M model with the physical O&M center, virtual O&M center, twin data platform and O&M service system is established. In the case of product fault classification, the maintenance mechanism is designed and the O&M process in different states is analyzed. Then the implementation process and key technologies are described in detail. Through the digital twin model, the virtual-real interaction and data dynamic update of O&M are realized. Finally, taking the key components of a certain type of EMU bogie as an example, the K-means clustering and Apriori algorithm are used to analyze the fault data. Moreover, the validity and feasibility of the proposed model are verified by applying the fault data to the digital twin architecture. The proposed model and key technologies can provide a new solution for the intelligent O&M of complex products.</p> <p> </p>

A mechanism-data hybrid-driven framework for identifying dynamic characteristic of actual chemical processes

As a prerequisite of accurate process analysis, prediction and optimization, the precise identification of process dynamic characteristics is of great significance. Traditional identification methods and data-driven technologies suffer from low accuracy and noise interference when facing the identification of actual chemical process. In this contribution, a mechanism-data hybrid-driven framework is developed to simultaneously capture the delay time and dynamic response of process variables. Combining non-parametric identification method, theoretical model and big data analysis technology, the proposed framework is able to derive more accurate and reliable identification results from the distributed control system (DCS) data directly, avoiding drawbacks brought by single method. The presented framework successfully identifies the flowrate dynamic properties of an actual deethanization process, and outperforms traditional approaches including finite impulse response model and Welch's averaged periodogram method. The contribution is significant for dynamic characteristic identification of actual chemical process, facilitating the developments of high-accuracy models for process prediction, control and intelligent optimization.

Retracted: Construction of Smart Higher Education Teaching Resources Using Data Analysis Technology in Unbalanced Data Environment.

[This retracts the article DOI: 10.1155/2022/2130623.].

Research on the Application Status of UAV in Agriculture

UAVs are widely used in agriculture. Through high-resolution image and data collection, they help farmers carry out crop monitoring and evaluation, precision agriculture, plant protection and crop spraying. However, there are still challenges such as high cost, low degree of agricultural mechanization, laws and regulations and privacy protection. Future development trends include cost reduction, improvement of data processing and analysis technology, integration with other agricultural technologies, autonomous flight and intelligent technology. The application potential of UAV is huge, which can improve agricultural efficiency and quality, and achieve sustainable development. UAV technology should be popularized and applied, and relevant problems should be solved.

Retracted: Quantitative Evaluation and Optimal Decision-Making Study on the Cultivation under the Environment of Humanistic Literacy Based on Advanced Data Analysis Technologies.

[This retracts the article DOI: 10.1155/2022/8918871.].

К ВОПРОСУ ПРЕПОДАВАНИЯ АНАЛИЗА ДАННЫХ И МАШИННОГО ОБУЧЕНИЯ СТУДЕНТАМ НАПРАВЛЕНИЯ ПОДГОТОВКИ "СОЦИОЛОГИЯ"

The article discusses the issues of teaching students of the direction of preparation «Sociology» modern information technologies for data analysis and machine learning. The actual educational discipline (module) «Data Analysis in Sociological Research» for bachelors and masters is presented, which forms competencies in the field of information technology and the skills to use them to solve problems ofprofessional activity in the field of sociology.

Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving

This study aims to enable cost-effective Internet of Things (IoT) system design by removing redundant IoT sensors through the correlation analysis of sensing data collected in a smart home environment. This study also presents a data analysis and prediction technology that enables meaningful inference through correlation analysis of data from different heterogeneous IoT sensors installed inside a smart home for energy efficiency. An intelligent service model that can be implemented based on a machine learning algorithm in a smart home environment is proposed. Herein, seven types of sensor data are collected and classified into sets of input data (six environmental data) and target data (power data of HVAC). By using the six new input data, the power data can be predicted by the artificial intelligence model. The model performance was measured using RMSE, and the gradient-boosting regressor (gb) model performed the best, with an RMSE of 22.29. Also, the importance of sensor data is extracted through correlation analysis, and sensors with low importance are removed according to the importance of sensor values. This process can reduce costs by 13%, thereby providing a design guide for a cost-effective IoT system.

Research on the innovation of ideological and political education for college students based on the Civic and Political Science Cloud Education Platform

Abstract Innovative ideological and political education for college students is to help better students improve their ideological quality and political literacy. In this paper, under the background of big data, the Civic and Political Cloud Education Platform is constructed using big data technology, which is mainly composed of four main parts: model layer, view layer, control layer, and basic services. Student behavior data is collected and analyzed through a collaborative filtering recommendation algorithm under big data collection and analysis technology, and performance simulation analysis is conducted for the Civic and Political Science Cloud Education Platform. From the performance analysis, when the number of concurrent users of the Civic Education Cloud Platform is 80-120, the average transaction processing per second is stable at 10.5 s. From the practicality, the Civic Education Cloud Platform built by big data can effectively explore the value of Civic Education teaching resources. This shows that the Civic and Political Science cloud education platform is an effective way to innovate and develop the ideological and political education of college students, and through this platform, the ideological quality and political literacy of college students can be effectively improved.

Searchable encryption algorithm in computer big data processing application

With the continuous development of computer technology, the amount of data has increased sharply, which has promoted more and more diversified data transportation and processing methods. At the same time, computer data analysis technology can effectively process data. This is reflected in the computer big data analysis technology not only can realize data visualization analysis, but also has data prediction and data quality management. The development of cloud computing network technology can not only provide convenience points for individuals, but also provide space for enterprises to store data. The emergence of keyword search encryption algorithms solves this problem. When users use keywords to search encryption algorithms, they can search for cipher text keywords to find the files or data they want in the cloud environment. At present, it has been widely used. In addition, this article also improves the keyword search plan and the user's query plan according to the dynamic changes of keywords, and proposes a user's multi-dynamic keyword search encryption plan. Through this program, users can search for encrypted files by keywords and change them, and the changed data will be dynamically updated. In this way, the program can realize multi-user data sharing, and can realize efficient search and dynamics.

Research on Space Image Fast Classification Based on Big Data

In order to improve the accuracy and effect of space image classification, the author proposes a space image classification method based on Big data analysis, aiming at the shortcomings of low accuracy and long time of current image classification. First, analyze the current research progress of image classification, find out the shortcomings of different classification methods, then collect aerospace images, preprocess the images, and use big data analysis technology to establish image classifiers, image classification was performed using an image classifier, and finally simulation experiments were conducted with other methods for image classification. The results indicate that: The average classification time of this method for aerospace images is 3.5 minutes, which saves 14 minutes and 29 minutes compared to traditional method 1 and traditional method 2, respectively. This indicates that this method has the shortest image classification time and improves the classification efficiency of aerospace images. This method has been proven to have high accuracy in image classification, the shortest classification time, and significant advantages compared to other image classification methods.

Research on the Application of Management Accounting in Logistics Enterprises in the Internet+ Era

This paper aims to study the application of management accounting in logistics enterprises in the era of Internet+. With the development of Internet+, logistics enterprises are facing increasingly fierce competition and management challenges. As an important management tool, management accounting provides important support and guidance for decision-making in logistics enterprises in this context. This study explores the application of management accounting in logistics enterprises and analyzes its roles in cost management, performance evaluation, budget management, fund management, and decision support. At the same time, this paper also discusses the innovative application of management accounting in the era of Internet+, such as the introduction of big data analysis and artificial intelligence technology. By properly applying management accounting tools and methods, logistics enterprises can improve operational efficiency, reduce costs, and further optimize management processes through innovative technological applications. This paper provides theoretical and practical references for the implementation of management accounting in logistics enterprises in the era of Internet+.

Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association.

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

Big data-based output-oriented teaching competency development for mathematics teacher trainees

Abstract The purpose of exploring output-oriented ways to develop the teaching ability of teacher-training students in mathematics is to help better them improve their teaching ability. Starting from big data analysis technology, this paper first introduces the principle of support vector machines and explains that many types of support vector machines are classified by solving the optimal objective function through the Lagrange multiplier method. Then, the principle of the particle swarm optimization algorithm is introduced, the steps and process of the PSO algorithm are given, and the PSO-SVM classification model is constructed by using the PSO algorithm to optimize the parameters of support vectors. Finally, the PSO-SVM model is used to construct and analyze the evaluation index system with the example of teacher-training students majoring in mathematics at YL University. From the indicators of professional foundation and instructional design, 40%, 39.32%, 14.31%, and 6.35% were evaluated at A, B, C, and D levels, respectively. From the indicators of teaching evaluation and reflective development, the four levels of A, B, C and D evaluations accounted for 40.84%, 24.77%, 15.98% and 18.41%, respectively. This indicates that in addition to theoretical and practical techniques, it is also necessary to train teacher trainees in teaching evaluation and reflective development to improve their teaching ability under the output-oriented approach.

Designing a platform for Tweet Collection, Analytics and Storage (TweetCASP)

In this era of big data, the streaming, storage, and analysis of large amounts of data present a variety of challenges. Several challenges must be addressed by designers of data-intensive systems in order to retrieve useful information. Collecting, storing, and analyzing data requires a collection and analytics platform comprised of an appropriate choice of data processing and analytics technologies in order to acquire meaningful insight. In this paper, we report on TweetCASP (Tweet Collection, Analytics and Storage Platfrom), which gathers tweets based on user-entered keywords using Twitter's Streaming API, providing an environment for real-time analytics on streaming data and permanently storing data in an Apache Cassandra NoSQL datastore to fulfill future batch-oriented data processing requirements. Moreover, The TweetCASP presents an example of a data-intensive system used by software developers, designers, and researchers for data collecting and analytics.

Impact of land use on bike-sharing travel patterns: Evidence from large scale data analysis in China

With bike sharing flourishing around the world, researches focus on the optimisation of station layouts, but seldom explore the dynamical features of residents’ bike-sharing travel habits. Land use factors probably indicate the activities and reflect the reasons behind residents’ travel habits; however, few studies provide empirical evidences, especially with big data analysis technology. Thus, we conduct a big data-driven empirical research to clarify residents’ travel patterns and the effects of land use thereon, which is based on the full system records of one typical bike-sharing city in China. K-modes clustering method is used to identify residents’ single and daily bike-sharing travel patterns. Three multinomial logit regression models are applied to exploring the selection mechanisms of the travel patterns. Eventually, we find that with the reference of ST pattern, the TPT-ENW pattern selection is most affected by the number of accommodation service places around travel destinations and one standard deviation increase will increase relative risk of this pattern by 1.57467. The MNT-SC pattern is most affected by the number of corporations and the other five single travel patterns are all significantly affected by the number of catering service places, which indicates commuting and dining are the travel purposes of these patterns. In addition, we find a multinomial logit model with individual fixed effects can explain residents’ daily travel pattern selection better than a pooled multinomial logit model. The analysis of residents’ travel routines explains the distribution of bike-sharing mobility and provides basis for bike-sharing system policy formulation.