Application Of Data Research Articles

Challenges of Using Synthetic Data Generation Methods for Tabular Microdata

The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models’ robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks—generator and discriminator—to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN’s sensitivity to training parameters and TVAE’s robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data.

High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

ABSTRACT High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s T 2 chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings.

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms.

A standard to report biological variation data studies- based on an expert opinion.

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to areporting standard, thereby providing a tool to be used toensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected toadapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

Development of a model for assessing the technical condition of Baghdad bridges

The research aims to develop a significant model for assessing the condition of bridges. This model may serve as a supplementary source of information in determining bridge condition ratings not only in Baghdad, but also in other regions with similar operating conditions. The authors also used the methods of mathematical statistics, probability theory, and mathematical theory of planning to conduct an experiment using the results of bridge survey. Currently, the Commission on Audit uses financial depreciation models to determine the service life of structures, with no alternative formulas available for predicting this parameter in the country. Therefore, the authors consider a new approach on the basis of available research on depreciation models. This approach is based on assessment of the condition of 30 structural elements of a bridge, as well as the results of inspections conducted by the Department of Public Works and Highway Bridges. The purpose was to determine the applicability of available data as meaningful variables in identifying a regression model to predict the condition of such bridges in Baghdad. Statistical data for determining the coefficients and dependent variables provided a relevant model for bridge condition assessment, which can be applied to identify the technical state of bridge structures.

Design Considerations and Flow Characteristics for Couette-Type Blood-Shear Devices

Cardiovascular prosthetic devices, stents, prosthetic valves, heart-assist pumps, etc., operate in a wide regime of flows characterized by fluid dynamic flow structures, laminar and turbulent flows, unsteady flow patterns, vortices, and other flow disturbances. These flow disturbances cause shear stress, hemolysis, platelet activation, thrombosis, and other types of blood trauma, leading to neointimal hyperplasia, neoatherosclerosis, pannus overgrowth, etc. Couette-type blood-shearing devices are used to simulate and then clinically measure blood trauma, after which the results can be used to assist in the design of the cardiovascular prosthetic devices. However, previous designs for such blood-shearing devices do not cover the whole range of flow shear, Reynolds numbers, and Taylor numbers characteristic of all types of implanted cardiovascular prosthetic devices, limiting the general applicability of clinical data obtained by tests using different blood-shearing devices. This paper presents the key fluid dynamic parameters that must be met. Based on this, Couette device geometric parameters such as diameter, gap, flow rate, shear stress, and temperature are carefully selected to ensure that the device’s Reynolds numbers, Taylor number, operating temperature, and shear stress in the gap fully represent the flow characteristics across the operating range of all types of cardiovascular prosthetic devices. The outcome is that the numerical data obtained from the presented device can be related to all such prosthetic devices and all flow conditions, making the results obtained with such shearing devices widely applicable across the field. Numerical simulations illustrate that the types of flow patterns generated in the blood-shearing device meet the above criteria.

HOW DOES THE GOVERNMENT PLAY A ROLE IN THE DEVELOPMENT OF THE BIG DATA INDUSTRY? BASED ON THE fsQCA RESEARCH OF 31 PROVINCES AND CITIES IN CHINA

This study employs fuzzy set qualitative comparative analysis to explore factors influencing the development of the big data industry in 31 provinces and municipalities in China. By analyzing eight representative variables across four dimensions—new infrastructure construction, data resources and applications, government services, and resource input—the study aims to uncover configurations and pathways that affect industry growth. The results reveal four effective strategies: combining infrastructure with data application, integrating government services with data application, coupling infrastructure with resource investment, and focusing solely on government services. Core variables such as optical cable length, policy environment, and service environment require governmental intervention to foster development. Therefore, continuous enhancement of new infrastructure construction, supporting the development of small and medium-sized Enterprises in big data, strengthening the big data industry and government service environment, and increasing investments in talent and capital are recommended to drive industry progression.

Enhancing Safety and Quality in College Sports Management Through Big Data and Artificial Intelligence (AI)

The purpose of this study is to explore how artificial intelligence (AI) and big data can be used to solve the twin issues of athlete safety and sports event quality in a college sports environment. Furthermore, this study attempts to fill the literature vacuum regarding the application and effectiveness of artificial intelligence and big data in improving safety and quality in collegiate sports administration by investigating possible synergies between these elements and the implementation of developed technologies. This qualitative study used a sampling method to conduct in-depth interviews with 18 sports administrators and commentators. Using coding and classification methods, the data were evaluated thematically with a focus on artificial intelligence and big data applications. Research has found that artificial intelligence and big data play a key role in proactively reducing injuries, optimizing athlete performance and enabling data-driven decision-making. It also identifies barriers and opportunities for integrating these technologies, revealing their dynamic potential. This study provides new perspectives on the relationship between safety and quality and the application of artificial intelligence and big data in collegiate sports management. It also highlights the ways in which these technologies have transformative potential in sport. The findings have important implications for educational programs and policy development aimed at managing responsible technology integration and preparing future professionals in the field of sport management.

Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data

Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment.

THE IMPACT OF SMALL AND MEDIUM-SIZED TOURISM ENTERPRISES DIGITAL TRANSFORMATION ON THEIR COMPETITIVENESS

At present, China's economy has shifted from a high-speed growth stage to a high-quality development stage. At the macro level, high-quality development takes the form of high-quality economic development, while at the micro level, it manifests as high-quality enterprise development. High-quality enterprise development is the foundation of high-quality economic development. This study explores the mechanism of the digital transformation of small and medium-sized tourism enterprises in terms of their competitiveness and analyzes the regulatory role of environmental uncertainty in the relationship between digital transformation and corporate competitiveness. From the perspective of tourism enterprises, it is necessary to improve the level of data application capabilities, build a digital talent training system, and transform organizational culture and management models; from the perspective of the government, it is necessary to improve the top-level design of digital transformation, build a favorable digital environment, and increase government support.

Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology and its operational efficiency. In response, this study introduces a novel application of the k-Nearest Neighbor (k-NN) algorithm to assess the reliability and applicability of solar panel simulation data which aimed to classify current states of partial shading, open, and short circuit conditions, alongside regression-based analysis for predicting specific operating parameters. In this research, a published dataset that involved various PV module configurations under different environmental conditions was tested and evaluated. The k-NN technique was applied to both classify the operational status and predict performance metrics of the modules. The diagnosis model demonstrated an accuracy of 99.2 % and an F1 score of 99.2 %, indicating a high degree of reliability in identifying the operational states of PV modules. Concurrently, the regression model exhibited a Root Mean Square Error (RMSE) of 0.036 and an R2 value of unity that showcased its effectiveness in predicting the operational parameters based on the simulation data. The concluded results are further enriching the effectiveness of simulation-based data generation to be endorsed and implemented before jumping into real experimental applications, in addition to highlighting the potential applicability of k-NN and machine learning for PV cells productivity statistical analysis.

Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic

The dependence in the tails of the joint distribution of two random variables is generally assessed using χ-measure, the limiting conditional probability of one variable being extremely high given the other variable is also extremely high. This work is motivated by the structural changes in χ-measure between the daily rate of return (RoR) of the two Indian airlines, IndiGo and SpiceJet, during the COVID-19 pandemic. We model the daily maximum and minimum RoR vectors (potentially transformed) using the bivariate Hüsler-Reiss (BHR) distribution. To estimate the changepoint in the χ-measure of the BHR distribution, we explore two changepoint detection procedures based on the Likelihood Ratio Test (LRT) and Modified Information Criterion (MIC). We obtain critical values and power curves of the LRT and MIC test statistics for low through high values of χ-measure. We also explore the consistency of the estimators of the changepoint based on LRT and MIC numerically. In our data application, for RoR maxima and minima, the most prominent changepoints detected by LRT and MIC are close to the announcement of the first phases of lockdown and unlock, respectively, which are realistic; thus, our study would be beneficial for portfolio optimization in the case of future pandemic situations.

Estimating forest aboveground biomass in tropical forests with Landsat time-series data and recurrent neural network

ABSTRACT The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests.

Seismic landslide susceptibility evaluation model based on historical data and its application to areas with similar environmental settings

Seismic landslide susceptibility evaluation models are usually built on the basis of historical sample data; however, the evaluation results are often unsatisfactory when the environmental settings differ between the historical sample data region and application region. Therefore, similarity between the environmental settings is important for the application of such models. In this paper, a seismic landslide susceptibility evaluation model was first built using data from the 2008 Ms 8.0 Wenchuan earthquake-induced landslide, and the model was then used to evaluate the 2022 Ms 6.8 Luding earthquake area. In addition, the grade of susceptibility is typically represented by the landslide density, which is insufficient for capturing the details of landslides, such as their sizes, frequencies, and spatial distribution patterns. The authors therefore use a large and concentrated landslide as the susceptibility grade for the Luding earthquake area. The test results demonstrate that these two areas have similar background environments. The area under the curve (AUC) value of the receiver operating characteristics (ROC) curve of the evaluation accuracy for the model applied to the Luding earthquake area is 0.889, which indicates relatively high accuracy. Besides, the results also demonstrate that the evaluations are consistent with the disaster situation of the Moxi Platform, Wandong Village, as well as the Dagangshan Hydropower Station area. Therefore, it is reliable to apply the susceptibility evaluation model based on the Wenchuan earthquake data to the Luding earthquake area. These results show that better evaluations can be obtained based on environmental similarity tests between the areas used for historical data modeling and areas to which the models are applied.

Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques

Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance. By implementing these techniques on car accident data, health organizations can identify and forecast a fatal event, enabling more efficient and effective allocation of limited health resources. Concurrently, enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions. Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents, ultimately contributing to improved road safety and better management of health resources. Moreover, the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction. Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.

Analysis of the Use of Artificial Intelligence in Software-Defined Intelligent Networks: A Survey

The distributed structure of traditional networks often fails to promptly and accurately provide the computational power required for artificial intelligence (AI), hindering its practical application and implementation. Consequently, this research aims to analyze the use of AI in software-defined networks (SDNs). To achieve this goal, a systematic literature review (SLR) is conducted based on the PRISMA 2020 statement. Through this review, it is found that, bottom-up, from the perspective of the data plane, control plane, and application plane of SDNs, the integration of various network planes with AI is feasible, giving rise to Intelligent Software Defined Networking (ISDN). As a primary conclusion, it was found that the application of AI-related algorithms in SDNs is extensive and faces numerous challenges. Nonetheless, these challenges are propelling the development of SDNs in a more promising direction through the adoption of novel methods and tools such as route optimization, software-defined routing, intelligent methods for network security, and AI-based traffic engineering, among others.

Divergences based Bayesian inference with censored data

. In this work, we deal with some Bayesian inference problems in the presence of right censored data. First, we propose a dual ϕ−divergence Bayes type estimators for parametric models and we establish their asymptotic normality. To establish this result, we need a uniform strong law of large numbers that we prove as well. Then, we consider the problem of prior distributions construction for model selection using ϕ−divergences. Finally, we consider the problem of the predictive density estimation on the basis of ϕ−divergences. We apply an expansion result of the generalized Bayesian predictive density on two parametric models widely used in survival analysis, namely the Weibull and the inverse Weibull model, under right censoring. We also check the performances of our proposed methods through simulations and real data applications. The results of these studies show that our proposed dual ϕ−divergence Bayes type estimators are more robust than other Bayesian estimators. Moreover, the generalized Bayesian predictive density performs better than the classical estimative density especially for the inverse Weibull model.

Predictions for Sparse Photometry of Jupiter-family Comet Nuclei in the LSST Era

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory will deliver high-quality, temporally sparse observations of millions of solar system objects on an unprecedented scale. Such data sets will likely enable the precise estimation of small-body properties on a population-wide basis. In this work, we consider the possible applications of photometric data points from LSST to the characterization of Jupiter-family comet (JFC) nuclei. We simulate sparse-in-time lightcurve points with an LSST-like cadence for the orbit of a JFC between 2024 and 2033. Convex lightcurve inversion is used to assess whether the simulation input parameters can be accurately reproduced for a sample of nucleus rotation periods, pole orientations, activity onsets, shapes, and sizes. We find that the rotation period and pole direction can be reliably constrained across all nucleus variants tested, and that the convex shape models, while limited in their ability to describe complex or bilobed nuclei, are effective for correcting sparse photometry for rotational modulation to improve estimates of nucleus phase functions. Based on this analysis, we anticipate that LSST photometry will significantly enhance our present understanding of the spin state and phase function distributions of JFC nuclei.

Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R2: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

Opportunities and Challenges of Utilising Big Data on SATUSEHAT Platform in Nursing: A Literature Review

The SATUSEHAT platform which is Indonesia Health Services (IHS) is a form of digital transformation initiated by the Indonesian Ministry of Health. This platform integrates the health data of all Indonesian people including medical records across health facilities which will develop into a national health data center and has the opportunity to become big health data for the Government. To face this, the nursing field must prepare for various opportunities and challenges. Objective: To provide an overview of the opportunities and challenges of utilizing big data through the SATUSEHAT platform in the nursing field. The method used in this research is literature review. The keywords used were Big data in nursing and The Application of big data for the future. Literature searches were conducted using the online database method starting from 2015-2022 through ScienceDirect, ProQuest and Clinical Key. As a result of the literature review with a case study design, it was found that opportunities for big data utilization include personalized care, nursing research development, data-based policy formulation, while challenges include increasing data processing and management competencies, integrating big data education curriculum, privacy and ethical issues and standardizing nursing terminology Recommendations It is hoped that further research can discuss the preparations that must be made to maximize the potential for big data utilization in the future.