Big Data Techniques Research Articles

Enhancing AI Model Accuracy and Scalability Through Big Data and Cloud Computing

Data's exponential growth and cloud computing advancements have significantly impacted artificial intelligence (AI) model development. This study investigates how big data techniques integrated with cloud computing enhance the scalability and accuracy of AI models across sectors such as healthcare, business, and cybersecurity. Adopting a qualitative methodology, the research examines secondary data from 2020–2024, including case studies and literature. Key findings reveal that cloud computing enables large-scale data processing with significant efficiency, achieving average speeds of 20–45 seconds for datasets ranging from 50–120 TB/day. AI model accuracy also improved across sectors, increasing by 20% on average—reaching 92% in cybersecurity, 90% in healthcare, and 85% in business applications. The study identifies deep learning algorithms as pivotal for leveraging cloud computing's flexibility, allowing for advanced data analysis and real-time insights. However, challenges in data security and privacy remain critical concerns. This research contributes by highlighting the transformative role of cloud computing in big data management and AI optimization, offering practical insights into enhancing predictive capabilities while addressing operational cost efficiency through scalable infrastructure. The findings emphasize the necessity of robust security protocols to mitigate risks and ensure sustainable AI applications. Future research should explore sector-specific implementations to refine and expand the practical utility of these integrated technologies.

Leveraging machine learning and big data techniques to map the global patent landscape of phage therapy.

Leveraging machine learning and big data techniques to map the global patent landscape of phage therapy.

Optimizing Rice Yield Quality Through Advanced Machine Learning Techniques in Precision Farming

Abstract: The continuously rising global trend in demographics is a challenge that modern agriculture has to contend with to feed the world by producing food produced with scarce inputs. In response, Advanced emerging technologies like machine learning (ML) and the IoT have birthed intelligent farming. This paper gives a decision-making framework for crop management, particularly using machine learning models to identify rice quality. Apache Kafka and Spark are used with the system and are coupled with big data techniques for enhanced data streaming and processing. Real-time classification of rice quality using various ML algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Feedforward Neural Networks (FFNN) are used. This research shows that this methodology significantly improves the decisionmaking approach in precision agriculture as a viable framework for future agricultural systems. Lastly, suggestions for future development aret presented regarding incorporating environmental information and utilizing the models in wider scouting practices.

Mapping and reconstruct suspended sediment dynamics (1986–2021) in the source region of the Yangtze River, Qinghai-Tibet Plateau using Google Earth Engine

Using remote sensing to measure suspended sediment concentration (SSC) in mountainous rivers can compensate for the scarcity of in situ sediment observations, providing valuable direct supplementation to observational records. However, for inland rivers, remote sensing SSC assessments face challenges such as data quality, long-term water body changes, environmental noise, flood events, and the transferability of local calibrations. Here, we introduce and apply remote sensing big data techniques using 12,445 cloud-free Landsat 5, 7, and 8 satellite images to calibrate SSC in the source region of the Yangtze River (SRYR). Utilizing Google Earth Engine, we implemented a series of image preprocessing techniques and water fraction methods to extract precise inland river water masks. Then we used unsupervised K-Means clustering and machine learning algorithms to model the relationship between water optical properties and SSC. By integrating these methodologies, we achieved an average relative calibration error of 0.26 for each optical cluster, and an average relative station deviation of 0.24 based on in situ measurements, minimizing SSC calibration to acceptable levels. Additionally, our results reveal that geomorphic patterns significantly influence sediment yield and transport by regulating sediment sources and sinks, fluvial morphology, and water-sediment connectivity. Over the past two decades, approximately 35.73 % of the sediment relative to the basin outlet discharge in the SRYR has been temporarily stored or confined within sediment sinks. These methods and findings hold significant implications for assessing and projecting fluvial sediment dynamics and the associated ecological and environmental issues in ungauged cold headwater regions.

Application of big data and artificial intelligence in visual communication art design.

In the era of continuous development of computer technology, the application of artificial intelligence (AI) and big data is becoming more and more extensive. With the help of powerful computer and network technology, the art of visual communication (VISCOM) has ushered in a new chapter of digitalization and intelligence. How vision can better perform interdisciplinary and interdisciplinary artistic expression between art and technology and how to use more novel technology, richer forms, and more appropriate ways to express art has become a new problem in visual art creation. This essay aims to investigate and apply VISCOM art through big data and AI methods. This essay proposed the STING algorithm for big data for multi-resolution information clustering in VISCOM art. In addition, the convolutional neural network (CNN) in AI technology was used to identify the conveyed objects or scenes to achieve the purpose of designing art with different characteristics for different scenes and groups of people. STING is a multi-resolution clustering technique for big data, with the advantage of efficient data processing. In the experimental part, this essay selected a variety of design contents in VISCOM art, including logo design, text design, scene design, packaging design and poster design. STING and CNN algorithms were used to cluster and AI-identify the design elements 16 of the design projects might contain. The results showed that the overall average clustering accuracy was above 82%, the accuracy of scene element recognition mainly was above 80%, and the accuracy of facial recognition was above 80%; this showed that this essay applied AI and big data to the design of VISCOM, and had a good effect on the clustering and identification of design elements. According to expert scores, these applications' reliability and practicality scores were above 70 points, with an average of about 80 points. Therefore, applying big data and AI to VISCOM in this essay is reliable and feasible.

EFFICIENT USE OF LANDSAT-8 DATA TO MINIMIZE CLOUD COVERAGE IN THE ANALYSIS OF SOYBEAN, CORN, AND COTTON CULTIVATION

This study aimed to manipulate databases from the Landsat-8 project to select orbital scenes with minimal cloud coverage aligned with the growth periods of cotton, corn, and soybean crops in the Cerrado biome. Employing an Extract, Transform, Load (ETL) approach, the research focused on extracting and transforming large datasets by integrating georeferenced information from IBGE and data from the Agricultural Risk Zoning (ZARC). The planting windows for the 2014 to 2019 harvests were identified with a 20% or lower climate risk, considering variables such as soil and climate conditions. The results indicated that the availability of images with low cloud coverage is inversely related to the planting windows, especially during critical periods. To streamline the processing, algorithms were developed in R and Python, and a script for automatic image downloading was implemented. Despite data standardization challenges, applying big data techniques proved essential for the analysis. This study enhances efficiency in agricultural planning and emphasizes the importance of further exploring remote sensing technologies in response to current food security and sustainability demands.

BIG DATA AND STATISTICAL APPLICATIONS

In recent years, modern developments in computer and cloud technologies have led to a significant increase in the quantity and speed of generated and stored information. This increase in the amount of information has introduced a new concept into our lives called "Big Data". Big Data offers significant advantages, especially for governments and businesses, in making informed decisions about their futures. However, the magnitude and diversity of this emerging data also bring about some challenges. Traditional database systems have been inadequate in processing these data formats, necessitating the need for new tools and techniques to overcome this challenge. Today, a wide range of techniques and technologies have been developed to collect, process, analyze, and visualize “Big Data”. These techniques and technologies encompass and draw upon various fields such as statistics, computer science, applied mathematics, and economics. Keywords: big data, visualization of data, structured data, big data technologies, big data techniques.

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model

Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O3). This study aims to elucidate global CO pollution in relation to health risks and the influence of natural events like wildfires. Utilizing artificial intelligence (AI) big data techniques, we developed a high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) model to estimate daily global CO concentrations at a high spatial resolution of 0.07° from June 2018 to May 2021. Our model integrated the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets, achieving desirable estimation accuracies with R-values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The analysis reveals that the CO concentrations were relatively high in northern and central China, as well as northern India, particularly during winter months. Given the significant role of wildfires in increasing surface CO levels, we examined their impact in the Indochina Peninsula, the Amazon Rain Forest, and Central Africa. Our results show increases of 60.0%, 28.7%, and 40.8% in CO concentrations for these regions during wildfire seasons, respectively. Additionally, we estimated short-term mortality cases related to CO exposure in 17 countries for 2019, with China having the highest mortality cases of 23,400 (95% confidence interval: 0–99,500). Our findings highlight the critical need for ongoing monitoring of CO levels and their health implications. The daily surface CO concentration dataset is publicly available and can support future relevant sustainable studies, which is accessible at https://doi.org/10.5281/zenodo.11806178.

Retraction Note: Distributed computing and big data techniques for efficient fault detection and data management in wireless networks

Retraction Note: Distributed computing and big data techniques for efficient fault detection and data management in wireless networks

Revolutionizing Early Detection: The Influence of Machine Learning in Esophageal Adenocarcinoma Diagnosis

Machine learning is an analysis technique of big data that uses computers and mathematical models to develop algorithms. These algorithms are applied in many situations and one of the most common uses is in developing prediction models. Machine learning is the activity of making prognosis of other data points which are related to the self using key factors from a dataset. It is designed to effectively predict the dysplasia within a given setting using machine learning algorithms on a dataset with primary focus on Barrett’s Esophagus. The study investigates the use of five classification methods: As per the classification, the techniques identified include Naïve Bayes, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors, and Logistic Regression. Barrett’s Esophagus Below are the methods applied using and without Principal Component Analysis (PCA) on the special dataset of Barrett’s esophagus. The primary objective is the identification of the effectiveness of each algorithm by comparing their functional characteristics such as F1 score, recall value, accuracy percentage, precision ratio, and specificity coefficient. From the results obtained from the experiments, two algorithms that yield good accuracy for predicting Barrett’s esophagus in this dataset are the SVM algorithm with PCA and Logistic Regression algorithm with PCA and both algorithms returned review ratings of 1. undefined This career has earned a maximum Score of 0.1000, Naïve Bayes perform well in the assessment with 0.964 of accuracy, it followed by K-Nearest Neighbors with 0.9349 and Logistic Regression with 0.923.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

Analysis of Factors Influencing Wait Times at Disneyland Resort

This paper aims to enhance the accuracy of wait time predictions and optimize park management at Disneyland, a popular leisure destination facing long wait times for attractions. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. These wait times adversely affect visitors' immediate experiences and overall evaluations, influencing their willingness to return. The study employs advanced data analysis and machine learning algorithms to develop high-precision wait time prediction models. Utilizing big data techniques, the research integrates multi-dimensional data, including weather, visitor flow, and historical wait times, to optimize resource allocation and improve operational efficiency. optimize resource allocation and improve operational efficiency and visitor satisfaction. The study further explores the application of these models in other domains such as healthcare, healthcare, and healthcare services. The study further explores the application of these models in other domains such as healthcare and transportation, demonstrating the potential for cross-disciplinary technology transfer to enhance overall service quality and efficiency. The findings underscore the significant impact of big data behavior analysis in theme park management, contributing to the findings underscore the significant impact of big data behavior analysis in theme park management, contributing to increased visitor satisfaction, enhanced competitiveness, and long-term sustainability of the park.

Big Data Analytics Challenges and Opportunities in Heart Disease Recognition: Novel Dimensionality Reduction with Classification Approach

Due to the current technological growth, a number of strategies have been developed, and more are being developed to eliminate problems that arise in many fields. Big Data techniques are employed to effectively stored health data due to the continual and massive volume of data created by the human body. Furthermore, the most important procedure is the classification of health data since it must be carried out precisely in order to diagnose cardiac disease early. The database images are various in size to reduce the dimension the Modified Principal Component Analysis (MPCA) Algorithm is used. the proposed MPCA algorithm is act as a feature selection model to pick features. One of the best and most effective techniques for classifying medical data is the Modified Deep Convolutional Neural Network (MDCNN). It has been shown to work for a variety of hospitalized patients. Consequently, the simulation results show that this proposal enhances classification accuracy in experimental research for the detection of heart ailment. Hence, the proposed method leads to an efficient usage of the resources and cost reduction. This approach assists the physician in taking suitable decision for giving a better treatment at right moment.

Big Data-driven MLOps workflow for annual high-resolution land cover classification models

Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The success of land cover classification models largely hinges on the data quality, coupled with the application of Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the maintenance of these models, a feature particularly beneficial in systems that require generating new models each year alongside the continuous integration of validated data. This article details the development of an end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.

Exploring Post-Experience Evaluation of Gastronomy Tourism Experiences Through Airbnb

ABSTRACT This research delves into factors impacting the post-evaluation of Airbnb gastronomic experiences based on expectation disconfirmation theory. Utilizing big data techniques, including topic modeling, sentiment analysis, image analysis, and conventional OLS regression methods, the study scrutinized 196,265 online reviews and 1,331 images from Airbnb Experiences Website. The findings reveal that review sentiments, image vibrancy, host credibility, and the count of reviews play pivotal roles in determining gastronomy experience ratings. Furthermore, Using Latent Dirichlet Allocation, the reviews revealed six main topics in order of importance: food city tours, social interactions, host, local food culture, beverage appreciation, and hands-on learning. These results illuminate the primary considerations of gastronomy tourists, providing guidance for hosts to better design their experiences and offering insights for other stakeholders in this sector.

Identifying city bus passenger ridership patterns: a mixed-method analysis

PurposeThis study uses big data analysis aimed at discovering city bus passenger ridership patterns. Hence, marketing managers can get sufficient insights to formulate effective business plans and make timely decisions about company operations.Design/methodology/approachThis study uses a mixed-method analysis to analyze the results. First uses the RFM (recency, frequency, and monetary) model combined with a big data technique (K-means) to analyze bus passenger boarding behavior. In order to improve the validity and quality of the research, this study also conducted interviews with senior managers of the bus company from which the data was obtained.FindingsThe study identifies six distinct groups of passengers with different boarding behaviors, ranging from “general passengers” to “most valuable passengers”. General passengers constituted the largest group. As such, they should be the main target for municipal governments when promoting bus ridership as part of energy conservation and carbon-reduction activities. This group of passengers should be encouraged to take public transport vehicles more, instead of relying on personal vehicles. The fourth group identified included elderly passengers with hospitals as their destinations. Bus companies can cooperate with municipal government to provide morning “medical bus” services for the elderly. Interviews with bus company managers confirmed that the analytical results of this study correspond with the observations, experiences, and actual business operating plans of bus companies.Originality/valueOnly few studies have analyzed passengers' boarding behavior applying a mixed-method analysis.

New Exploration of Legal Research Based on Big Data Technique

New Exploration of Legal Research Based on Big Data Technique

Assessment of soil fertility in Xinjiang oasis cotton field based on big data techniques

Assessing soil fertility through traditional methods has faced challenges due to the vast amount of meteorological data and the complexity of heterogeneous data. In this study, we address these challenges by employing the K-means algorithm for cluster analysis on soil fertility data and developing a novel K-means algorithm within the Hadoop framework. Our research aims to provide a comprehensive analysis of soil fertility in the Shihezi region, particularly in the context of oasis cotton fields, leveraging big data techniques. The methodology involves utilizing soil nutrient data from 29 sampling points across six round fields in 2022. Through K-means clustering with varying K values, we determine that setting K to 3 yields optimal cluster effects, aligning closely with the actual soil fertility distribution. Furthermore, we compare the performance of our proposed K-means algorithm under the MapReduce framework with traditional serial K-means algorithms, demonstrating significant improvements in operational speed and successful completion of large-scale data computations. Our findings reveal that soil fertility in the Shihezi region can be classified into four distinct grades, providing valuable insights for agricultural practices and land management strategies. This classification contributes to a better understanding of soil resources in oasis cotton fields and facilitates informed decision-making processes for farmers and policymakers alike.

Innovative approaches in forensic accounting: The role of data analytics

Abstract The integration of data analytics into forensic accounting has revolutionized the detection and prevention of financial fraud. This paper conducts a comprehensive analysis of recent advancements in this field, highlighting the application of machine learning, data mining, and big data techniques in identifying fraudulent activities. By reviewing the latest research and examining case studies, we demonstrate the enhanced accuracy and efficiency these technologies offer over traditional methods. The findings suggest that financial institutions should adopt these advanced tools to mitigate fraud risks and improve overall financial security. The paper also explores future research directions, emphasizing the need for developing hybrid models and real-time detection systems to further enhance fraud detection capabilities.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.