Big Data Analysis Model Research Articles

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.

Urban Green Space Planning and Design Based on Big Data Analysis and BDA-UGSPD Model

Urban Green Space Planning and Design Based on Big Data Analysis and BDA-UGSPD Model

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.

An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

Big Data Analytics Model Using Artificial Intelligence (AI) and 6G Technologies for Healthcare

Big Data Analytics Model Using Artificial Intelligence (AI) and 6G Technologies for Healthcare

Big Data Analytics for the Moroccan Water Actors: Towards an Adoption Model

This paper delves into the adoption of Big Data Analytics (BDA) within the Moroccan water management sector, specifically examining data collection, management flow, and decision-making processes. Through insightful interviews with representatives from the public sector responsible for water management, this study investigates the pivotal role of effective data governance in optimizing water resources[1]. Given the centrality of natural resources, particularly water, in national development strategies, the imperative for robust water governance is underscored. The impact of climate change further highlights the need for sufficient data collection and an efficient data management system[2]. In the management of natural resources, the accumulation of extensive data necessitates the application of BDA technology for streamlined processing. While BDA provides a solution for optimal data utilization, this article emphasizes the importance of maintaining control over processing and decision-making to ensure the success of functional and technical designs. The outcomes contribute to the formulation of an adoption model for BDA in the Moroccan water sector, facilitating well-informed and strategic decision-making for sustainable water resource management.

Application and practice of personalized education based on big data analysis in Java object-oriented program design

This paper delves into the application and practice of personalized education based on big data analysis in the teaching of Java object-oriented programming. By constructing a big data analysis model, we have achieved precise collection and processing of student learning data, thereby tailoring personalized learning paths and resource recommendations for each student. The aim of this paper is to analyze the implementation effects of this innovative educational model in the teaching of Java object-oriented programming, with the hope of providing valuable insights for improving teaching quality and student learning outcomes. Furthermore, we also explore the advantages of applying Java object-oriented programming in personalized education. The object-oriented nature of Java makes program design more aligned with human thinking patterns, aiding in the development of students' logical thinking and innovative capabilities. Additionally, the widespread use of the Java language also offers expansive opportunities for students' future career development.

Research on the Influence of Integrative Art Therapy on Adolescents’ Mental Health Based on Big Data Analysis

Abstract Integrative art therapy has achieved great results in improving neurosis, palliative care, mental development of special children, and mental health education of adolescents. Integrative art therapy is an applied technique that integrates artistic creation and psychotherapy. This topic studies the influence of integrated art therapy on adolescent mental health based on big data analysis, and establishes a big data analysis model of adolescent mental health. Through DM(data mining), the most valuable information is extracted, expressed in an easy-to-understand way, and analyzed and evaluated accordingly. In this process, the information should be filtered, hoping to get the information that meets the purpose of mining. Descriptive mining is used to describe the general properties of the mined data. If we want to make predictions by inferring the current data, we should use predictive mining. The results show that the scores of each item factor of SCL-90 self-rating scale in the experimental group have been improved obviously. It is mainly manifested in: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, hostility, terror and paranoia. The scores of these seven items have all dropped below 1.5. The median probability of using C4.5 model to predict the mental state as “abnormal” is 0.8135, and the lowest probability of using BPNN (BP neural network) model to predict the mental state as “abnormal” is 0.6082. This provides an idea for designing a psychological crisis prevention system based on the comprehensive C4.5 and BPNN technologies.

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

Retracted: Optimization of Ecological Mode of College Students’ Innovation and Entrepreneurship Education Based on a Big Data Analysis Model

Retracted: Optimization of Ecological Mode of College Students’ Innovation and Entrepreneurship Education Based on a Big Data Analysis Model

Contemporary High-Performance Computing for Big Data Applications

High-performance computing (HPC) involves leveraging parallel data processing to enhance computer performance and handle difficult tasks. HPC meets these aims by pooling computing capacity, enabling efficient, reliable, and prompt execution of even complex programs according to user demands and expectations. The rapid growth of HPDA in many sectors has led to the extension of the HPC market into new territory. HPC as well as Big Data systems differ not just in terms of technology but also in ecosystems. Extensive research in this sector has led to the emergence of various Big Data analytics models in recent years. As Big Data analytics spreads across several fields, new challenges about the usefulness of analytical paradigms also emerge. This article discusses the key analytical models, as well as the difficulties and challenges associated with high-performance data analytics. This research work aims to identify the factors influencing the integration of HPC with big data, including present and future trends. The study also proposes an architecture for big data with HPC convergence based on design principles.

A Distributed Big Data Analytics Models for Traffic Accidents Classification and Recognition based SparkMlLib Cores

In this paper, our focus is on predicting the severity of traffic accidents, which represents a significant advancement in road accident management. Addressing this issue holds crucial implications for emergency logistical planning within urban areas. To assess accident severity within congested settings, we analyze the potential consequences of accidents, aiming to enhance the effectiveness of accident management protocols. In the context of this study, we introduce a real-time big data project. Our approach involves the implementation and comparison of various machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN). The objective is to accurately classify and predict the severity of traffic accidents. Our methodology revolves around the real-time capture of incoming datasets, which are then stored within a Hadoop Distributed File System cluster. Subsequently, we leverage the core functionality of Spark MLlib, making use of pre-implemented Lambda functions. Throughout the project, classification and recognition tasks are conducted as part of the data locality processing paradigm. To validate our approach, we utilize a confusion matrix, which enables us to gauge the interclass impacts among Pedestrians, Vehicles or pillion passengers, and Drivers or riders. For empirical validation, we employ the TRAFFIC ACCIDENTS_2019_LEEDS dataset sourced from the Road Safety Department of Transport. This dataset facilitates the classification of severity predictions into three distinct categories: Pedestrian, vehicle or pillion passenger, and driver or rider. Notably, our experiments reveal impressive results. The Random Forest algorithm achieves an accuracy rate of 93%, outperforming SVM at 82% and ANN at 87%. Furthermore, in terms of precision-recall metrics, Random Forest also excels with a score of 93.82%, compared to SVM's 82.22% and ANN's 87.88%.

An Optimized Ensemble Support Vector Machine-Based Extreme Learning Model for Real-Time Big Data Analytics and Disaster Prediction

An Optimized Ensemble Support Vector Machine-Based Extreme Learning Model for Real-Time Big Data Analytics and Disaster Prediction

Retracted: Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

Retracted: Big Data Analytics and Discrete Choice Model for Enterprise Credit Risk Early Warning Algorithm

An Efficient Big Data Visualization Deep Learning Architecture Model for Path Selection of College Students through Moral Education

Visualization technology can be used to present the analysis results in a more intuitive and easy-to-understand way, which can help educators to better understand the moral education needs of college students, and adjust their teaching strategies accordingly. The combination of big data analysis and visualization technology can also help to improve the efficiency and effectiveness of moral education in colleges and universities. The research on the moral education path selection of college students based on big data visualization has great significance for promoting the development of moral education in colleges and universities, and for cultivating high-quality talent with good moral character. This paper proposed an Optimization model for big data analytics for moral education. The data associated with moral education and information are stored in cloud with the big data. The stored big data visualization process is performed with the optimization model for the feature extraction. The optimization is performed with an integrated Flamingo and weighted black widow Optimization model. The proposed model is stated as the Integrated Flamingo Black Widow (IFBW) model. The performance of the IFBW model is implemented with the deep learning Restricted Boltzmann Machine (RBM) architecture. Simulation analysis stated that IFBW model achieves a higher classification accuracy rate of 99% with a minimal error rate.

Deep Learning-Based Big Data Analytics Model Based on Teaching Reforms in Three-Dimensional Composition

With the development of online education and big data analysis, new teaching models and methods have emerged. The integration of online and offline teaching modes based on big data analysis has become an effective way to promote teaching reform and practice in the field of three-dimensional composition. It is important to incorporate teaching reform into the teaching of three-dimensional composition to improve the quality of education and better prepare students for their future careers. This paper evaluated the contribution of teaching reform to the improvement of student performance. This paper designed a Deep Learning (DL) big data analytics model for data clustering and classification. The student performance is monitored for both online teaching and offline teaching classes. The collected data is clustered with the directional clustering process for the computation of feature space. With the estimated feature space value Hidden Markov Model (HMM) is implemented for the estimation of statistical data derived from the feature spaces. The extracted data were applied over the RESENT- 50 model for the classification of students’ performance. The data analysis with DL model stated that student performance in offline teaching is more significant than offline teaching in 3-dimensional aspects.

Engineering archive management model based on big data analysis and deep learning model

Abstract In the background of the era of big data, the information management system of engineering archives has become more comprehensive and perfect because of the application of information technology. The application of deep learning model makes the management of engineering archives more systematic, scientific and standardized, which greatly improves the quality and efficiency of engineering archives management. The progress of society and the development of the times have put forward higher requirements for digital storage technology. This paper combines the characteristics of the new technological era, analyses the characteristics of traditional information management in the context of data processing, artificial intelligence, deep learning and other data, proposes a method for developing and managing web archives based on Bootstrapping technology, introduces an information meta-evaluation mechanism to improve the quality of mining, and uses a long and short-term memory model to extract multi-type fine-grained archival information elements in the corpus. Finally, the Alex network was established to manage the archives in a categorised manner. The experimental results show that the query results of the proposed method for the target archives are 100% accurate, and the query time for individual archives is basically within 5s, which has good management effect.

A Review onCloud Gaming

: The present paper mainly focuses on literature review of cloud gaming. cloud gaming model it emerges with the advancement of communication technologies and cloud computing. Moving towards PCs, smart phones consoles tablets, people can have opportunity to use the games via data streaming, despite of the computing power of devices. The Internet is the style of communication between the game, and device hosted on a cloud environment. The big data analytics models it is a having ability to work with these data, and provides inputs for predictive actions and assertive analysis. A technique is shown in this study to indicate and analyzing the players’ patterns in a virtual environment to optimize user experience. Using the visualization tools and Hadoop technology on a Cloud based cluster.

A Proposed Big Data Analytics Model for Crimes Predication based on Spatial and Temporal Criminal Hotspot

A Proposed Big Data Analytics Model for Crimes Predication based on Spatial and Temporal Criminal Hotspot

Prediction of Electric Load Neural Network Prediction Model for Big Data

In the background of information network and big data era, the digitalization and information network level of China's financial industry has been improved, and many advanced big data analysis technologies have been derived, among which mobile big data analysis model is the most widely used. Based on the concept of financial investment, mobile big data analysis model can not only improve the accuracy of financial data analysis, but also quickly establish the financial investment evaluation and prediction model, providing a reliable basis for financial investment and financial development strategy. In this regard, the author combines relevant literature and his own work experience to conduct a detailed study on mobile big data analysis model based on financial investment concept. Mobile big data analysis model is a kind of data analysis model established based on big data technology, which has the characteristics of strong data collection ability, high accuracy of data statistics and analysis, and high efficiency of data application. Based on the concept of financial investment, the mobile big data analysis model can not only improve the financial investment analysis function of the mobile big data analysis model, but also improve the financial risk analysis ability of the model, which provides a reliable basis for financial investment and financial risk management. Therefore, the following briefly outlines financial investment, then analyzes the importance of building mobile big data analysis model based on financial investment concept, and finally analyzes the method of building mobile big data analysis model based on financial investment concept and briefly discusses the typical application of the model.