Analytical Model Data Research Articles

Retraction Note: Deep learning based big medical data analytic model for diabetes complication prediction

Retraction Note: Deep learning based big medical data analytic model for diabetes complication prediction

Empirical investigation of a data analytics model in medical settings

This article centers around the development and analysis of machine learning and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of machine learning and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain.

Beyond Risk: A Measure of Distribution Uncertainty

This paper addresses the critical yet often overlooked concept of distribution uncertainty (ambiguity) in decision making, emphasizing its importance alongside traditional outcome uncertainty (risk). It introduces a novel quantitative measure of ambiguity that accurately captures distribution uncertainty. This measure enhances empirical models, yielding more reliable parameter estimates and improving decision-making processes. The study demonstrates the practical value of this ambiguity measure using financial market decision making as an example. The measure helps identify and adjust for uncertainties in underlying distributions, supporting more robust financial models and better risk management. The findings advocate for integrating ambiguity considerations into data analytics models and developing more reliable methodologies for empirical research and practical applications. This study promotes a nuanced understanding of uncertainty, offering significant implications for research methodologies and practical risk management across various fields.

Online Learning Based on Learning Analytics in Big Data for College English Language Teaching

This study addresses the challenges of enhancing the quality of education and improving the overall student experience in online English language teaching sessions. Current approaches often struggle with session initiation, real-time data processing, and personalized learning experiences. To tackle these issues, the study proposes a manifold learning data analytics model (MLDAM). This innovative method leverages classifier tree learning to distinguish between trivial and non-trivial aspects of student learning experiences and session data. MLDAM integrates multi-dimensional data extraction, classification learning, and impact evaluation to enhance the effectiveness of online English language teaching. The model analyzes data from 176,009 English phrases across 36 online teaching sessions, focusing on improving session accessibility, student comprehension, and suggestion accuracy. Using an iterative training process based on student performance and feedback, it continuously extracts and processes multiple types of data to refine teaching strategies. Results show notable improvements: a 14.74% increase in classification accuracy, an 8.73% increase in data extraction ratio, an 11.84% reduction in feature discard, a 10.57% decrease in initialization time, and a 13.17% reduction in classification time. These metrics demonstrate MLDAM’s ability to efficiently process and analyze session data, enabling real-time adjustments during lessons. The model optimizes data utilization, making learning more responsive and adaptable. It enhances the precision of impact evaluations, facilitating targeted course adjustments and personalized learning experiences. This research presents a comprehensive, data-driven methodology for improving teaching quality and student outcomes in virtual English classrooms.

A review of big data analytics models for assessing non-pharmaceutical interventions for COVID-19 pandemic management

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions.

New Method for Identification and Response to Infectious Disease Patterns Based on Comprehensive Health Service Data

Infectious diseases continue to pose a major threat to global public health and require early detection and effective response strategies. Despite advances in information technology and data analysis, the full potential of health data in identifying disease patterns and trends remains underutilised. This study aims to propose a comprehensive new mathematical model (new method) that utilises health data to identify infectious disease patterns and trends by exploring the potential of data-driven care approaches in addressing public health challenges associated with infectious diseases. The research methods used are exploratory data collection and analytical model development. The research results obtained mathematical models and algorithms that consider data of period, time, patterns, and trends of dangerous diseases, statistical analysis, and recommendations. Data visualisation and in-depth analysis were conducted in the research to improve the ability to respond to infectious disease threats and provide better decision-making solutions in improving outbreak response, as well as improving preparedness in addressing public health challenges. This research contributes to health practitioners and decision-makers.

Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security

ABSTRACT In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).

Big data analytics for image processing and computer vision technologies in sports health management.

Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. The fitness and health of professional athletes are analyzed using information from a publiclyavailable sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

A Physics-Based Model-Data-Driven Method for Spindle Health Diagnosis, Part I: Modeling of Geometric Faults

Abstract The spindle determines the performance of machine tools; hence, monitoring its health is essential to maintain machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location are correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyze the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III.

Wasserstein Robust Classification with Fairness Constraints

Problem definition: Data analytics models and machine learning algorithms are increasingly deployed to support consequential decision-making processes, from deciding which applicants will receive job offers and loans to university enrollments and medical interventions. However, recent studies show these models may unintentionally amplify human bias and yield significant unfavorable decisions to specific groups. Methodology/results: We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in the equality of opportunity criterion. We use a type-[Formula: see text] Wasserstein ambiguity set centered at the empirical distribution to represent distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We show that the model is equivalent to a mixed binary conic optimization problem, which standard off-the-shelf solvers can solve. We propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur binary variables to improve scalability. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the label and sensitive attribute uncertainties. We numerically examine the performance of our proposed models on five real-world data sets related to individual analysis. Compared with the state-of-the-art methods, our proposed approaches significantly improve fairness with negligible loss of predictive accuracy in the testing data set. Managerial implications: Our paper raises awareness that bias may arise when predictive models are used in service and operations. It generally comes from human bias, for example, imbalanced data collection or low sample sizes, and is further amplified by algorithms. Incorporating fairness constraints and the distributionally robust optimization (DRO) scheme is a powerful way to alleviate algorithmic biases. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869] and the Chinese University of Hong Kong [Grant 4055191]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0230 .

Covid-19 infection deficiency based on risk prediction using adaptive social spider featured decisive convolution neural network

The novel coronavirus disease which originated has been medical as Covid 19 by the World Health Organization (WHO). The early onset in December 2019. It revealed that. A virus has been the causative factor for this pandemic. A phylogenetic research has been carried out with achievable based on data analytic model. In this situation, individuals with this disease have improved more features produce poor classification accuracy. To resolve this problem, we propose an adaptive social spider featured decisive Convolution Neural network for Covid-19 deficiency infection based on risk prediction to enhance the prediction and evaluation of infectious diseases. Both GIS and the Covid data sets are used in the process. The Social spider Feature Selection (SSFS) selects features depending on Disease deficiency infection rate (DDIR) and Spreading habitual factor (SHF). By selecting these features decisive Convolutional Neural Classifier (DCNN) trains the value to trained set to classify the risk based on the feature predicting weights. This proposed system produce higher prediction rate compared to the other methods as well in precision and recall rate.

Implementation of machine learning techniques with big data and IoT to create effective prediction models for health informatics

As a result of the availability of healthcare data in sheer size, big data analytics has to grow regularly in this industry to ensure new and effective opportunities. This is helpful in providing early prevention, prediction, and detection of disease, thus helping in the enhancement of the overall life quality of the individuals. Likewise, in this paper, a machine learning-based big data analytics model is developed for predicting multi-diseases to provide a better decision support system for various healthcare applications. This developed framework utilizes the MapReduce framework, where the map phase performs feature extraction and the reduce phase performs feature selection for the purpose of handling and processing big data. The required healthcare data is collected from external web sources. In the map phase, the statistical features and the Principal Component Analysis (PCA) features are extracted. In the reduction phase, the optimal features are selected with the aid of the developed Hybrid Flower Pollination Bumblebees Optimization Algorithm (HFPBOA). Then, the Ensemble Learning (EL) model is developed to predict the multi-diseases. Moreover, the parameters present in the EL classifiers are optimized by using the same HFPBOA. The final prediction output is obtained by averaging the weight function between the outputs of the NN, KNN, and fuzzy classifier. Thus, the offered model attains 40.1%, 28.7%, 23.6%, and 10.5% improved than SSA-EL, DOA-EL, BOA-EL, and FA-EL respectively in terms of best value. The effectiveness computed for the developed multi-disease prediction framework is guaranteed by comparing the results among the recently developed prediction approaches.

Adverse Childhood Experiences and Chronic Low Back Pain in Adulthood: The Role of Emotion Regulation

Chronic low back pain (cLBP) is characterized by biopsychosocial determinants that collectively result in a substantial burden at the individual, community, and health care system levels. A growing body of literature suggests that childhood adversity is longitudinally associated with the development and maintenance of various chronic pain conditions in adulthood. Little research has investigated the psychological processes that might underlie the association between adverse childhood experiences (ACEs) and cLBP. Emotion regulation comprises a substantive part of the subjective experience of pain and may be a potential mechanism through which ACEs contribute to cLBP etiology and maintenance. Thus, the current study examined the extent to which emotion dysregulation mediated the relationship between ACEs and pain severity (pain at rest and movement-evoked pain) in adults with cLBP. Participants included 183 adults (53.0% female, 62.5% non-Hispanic Black) between the ages of 18 and 85 with cLBP. Participants self-reported on ACEs, pain, difficulties in emotion regulation (DER), depression, and completed brief physical function tasks. In data analytic models, sociodemographic variables were included as covariates. Analyses revealed that emotion regulation mediated the relationship between ACEs and cLBP severity at rest (indirect effect = .15 [95% CI {.06–.25}]) and with movement (indirect effect = 1.50 [95% CI {.69–2.57}]). Findings suggest ACEs are linked to cLBP severity in adulthood through DER. This aligns with research demonstrating that childhood maltreatment can lead to DER, which perpetuate over the lifespan to impact adult health outcomes. PerspectiveThis study presents emotion dysregulation as a psychological pathway through which childhood adversity may contribute to cLBP in adulthood. This work may bolster our understanding of social experiences as risk factors for chronic pain, while identifying targets for clinical intervention. Trial RegistrationThis study utilized baseline data collected as part of a parent trial titled “Examining Racial and SocioEconomic Disparities in Chronic Low Back Pain” (ClinicalTrials.gov ID: NCT03338192).

IoT system development for heart rhythm monitoring and cardiovascular risk estimation

The research focuses on addressing the global issue of cardiovascular diseases. The key variable under consideration for predicting cardiovascular diseases is heart rate variability (HRV). Leveraging the widespread adoption of IoT in various applications, particularly in the health sector, the study proposes the design and implementation of an IoT system for HRV monitoring. The research unfolded in four methodological phases: exploration and selection of technologies, definition of the IoT architecture, development of the prototype, and verification of its functionality. The implemented IoT system adheres to the conventional 4-layer IoT architecture: capture, storage, analysis, and visualization. Heart rate data is periodically acquired using a heart rate sensor and an Arduino-compatible board. The storage layer employs a non-relational database to store the captured data. The analysis layer extracts metrics related to HRV (High: RR <750 ms, Moderate: RR 750–900 ms, Low: RR >900 ms) by applying and delivering quantitative results from clustering algorithms such as machine learning models to evaluate data distribution. Risk levels indicate specific patient metrics. Thus, a 75-year-old patient exhibits an average HR of 75.56, Avg. RR of 795.42, falling into Cluster 1 with a risk value of 1.0. Similar detailed metrics and risk stratifications are presented for patients aged 68, 46, 37, and 18, demonstrating the system's robustness and efficacy in assessing cardiovascular risk. The visualization layer enables real-time observation of physiological variables, risk metrics, and results from data analytics models. The distinctive features of the results lie in the portability advantages of the IoT system, utilizing free hardware and software tools. This facilitates easy replication and utilization of the proposed system in medical campaigns, specifically for the early detection of cardiac conditions. The portable IoT system, leveraging free tools, enhances predictive capabilities for early cardiovascular risk detection globally

Novel applications of various neural network models for prediction of photovoltaic system power under outdoor condition of mountainous region

The geography and landscape of mountainous locations are frequently varied, which causes uneven solar radiation exposure in different places which leads to photovoltaic (PV) power generation variation drastically. Therefore accurate PV power prediction is essential for industries in optimize energy production, Energy Planning and Grid Integration, Energy Storage Integration in such diverse conditions. This study aims to predict the power produced by a 2.680 kWp PV under outdoor condition in mountainous region at different time intervals of 10 seconds, 1 minute, 30 minutes, 1 hour and 1 day using different data analytics models. The results show that the radial basis neural network was the most effective model for the intervals of 10 seconds, 1 minute, 30 minutes, and 1 day. Its RMSE was 19.11 W for the 10-second interval, 22.83 W for the 1-minute interval, 25.94 W for the 30-minute interval, and 9.08 W for the 1-day interval. With an RMSE of 23.22 W, the Kernel Ridge Regressor had the best performance for the 1-hour period. This suggests that the model with only two parameters such as temperature and irradiance predict PV power more accurately. The findings can be applied to improve PV system design and performance by precisely forecasting the systems' power output in different weather scenarios.

Retraction Note: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Retraction Note: Financial risk assessment to improve the accuracy of financial prediction in the internet financial industry using data analytics models

Validation of a digital twin: Part distortion in heat treatment

This paper presents a study on multi-process manufacturing optimisation, employing a sophisticated data analytic model based on Gaussian Processes. Through strategically designed aluminium trials and rigorous model evaluation using leave-one-out cross-validation, the efficacy of the model in identifying optimal parameters is demonstrated. An interactive dashboard, powered by the Anaconda Panel(c) library, enables real-time insights into the impact of varying parameters on outcomes. Experimental validation showcases the model’s ability to reduce distortions yet highlights the dynamic nature of manufacturing processes. The iterative refinement of optimal parameters based on real-world observations underscores the adaptability of the model. This study emphasises the importance of combining advanced data analytics with practical experimentation for enhanced precision in modern manufacturing, paving the way for further advancements in the field. Real-world validation of predicted optimal parameters reveals unexpected distortions, highlighting the dynamic nature of manufacturing processes and the necessity for continuous refinement. Further analysis uncovers a discrepancy in distortion outcomes, emphasising the need for vigilant investigation and iterative refinement. By incorporating the latest experimental findings, the data analytic model has generated updated optimal parameters, effectively minimising part distortion. This study underscores the significance of data-driven decision-making and the continuous integration of new experimental results in the pursuit of optimal manufacturing parameters, paving the way for further advancements in manufacturing optimisation.

The significance and application of data analytics models for strategic management

In today's global landscape, the survival and success of organizations depend significantly on the effective use of their data assets for informed strategic decision-making. The exponential proliferation of data and its multiple uses present profound challenges and opportunities. Proficient management, processing, and use of this vast reservoir of data have emerged as a paramount concern in today's world. At the same time, the wise application of data analytics techniques and frameworks offers compelling strategic advantages. Identifying and deploying the most relevant data analytics methodologies within the realm of strategic management presents a formidable challenge for senior executives. This study examines the central role of data analytics in strategic decision-making and explores how organizations can leverage it to gain a competitive advantage. In addition, the research examines recent literature that sheds light on the various dimensions of integrating data analytics into strategic management. The study also provides selected illustrative use cases and instances of strategic management at the corporate, business, and functional levels. In particular, it presents a spectrum of both quantitative and qualitative data analysis techniques to underscore the availability of frameworks that facilitate the integration of data analysis into strategic management and decision-making processes.

Retracted: Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Retracted: Big Data Analytics Model for Distributed Document Using Hybrid Optimization with <i>K</i>‐Means Clustering

Cloud Computing Assisted Mobile Healthcare Systems Using Distributed Data Analytic Model

Distributed cloud technologies enable mobile healthcare applications to support end users constantly. Data from electronic health records is made available through combination of user needs and heuristic mining. Because of inefficient data storage and reorganization, service and recommendation failures occur prematurely. A Distributed Data Analytics and Organization Model (DDAOM) is established in this article as a solution to the inefficiency of managing large amounts of data. Using this method, errors caused by performing several computations or storing large amounts of data in the medical field are minimized. Data organization and mining under predetermined schedules or factors provide information relevant to user services. One-to-many computations with varying input and output data allocations (for services) are executed in federated learning. The local input from several edges may be handled by allocating storage in a decentralized manner. The federated learning system uses the memory of past states to direct the allocation. Differentiating the states is necessary to allocate services and prevent mining in certain areas. With the help of realistic learning iterations, state management is maintained, guaranteeing the smooth deployment of services. Delays in storage and mining, uneven service provisioning, and service backlogs are used to evaluate the effectiveness of the suggested model.