Data Analysis Applications Research Articles

Improving Students’ Engagement with Feedback in Mathematics Classrooms: Based on Big Data Analytics

With the shift in educational paradigms from “teaching-centered” to “learning-centered”, mathematics classrooms are undergoing significant transformations. Feedback is a crucial factor influencing students’ learning and academic achievement, and students’ active engagement is considered a key prerequisite for effective feedback. However, current research has not fully explored how students engage with feedback in mathematics classrooms or the factors that influence the engagement. Additionally, providing timely and meaningful feedback remains a significant challenge for teachers. This article examines the nature of feedback, analyzes the critical role of student engagement with it, and identifies key factors influencing this engagement in mathematics classrooms. Based on the application of big data analysis, the study proposes strategies to enhance students’ engagement with feedback, aiming to promote mathematics learning and teaching. These findings provide a theoretical basis for improving students’ engagement with feedback and for guiding the strategic design of interventions aimed at improving this engagement.

Exploration of Micro-specialty Construction Based on Big Data and AI from the Perspective of Medical and Industrial Integration: A Case Study of Intelligent Technology Aging Application Micro-specialty in Anhui Sanlian University

With the aggravation of population aging, the demand of the elderly for old-age services is becoming increasingly diversified and personalized, which poses a severe challenge to the traditional old-age service model. In this context, it is particularly important to build a micro-specialty of "intelligent technology suitable for aging application", which combines big data analysis with artificial intelligence technology. Based on the practical exploration Faculty of Modern Health Care Industry College of Anhui Sanlian University in the field of health care industry, this paper deeply analyzes the social background and necessity of micro-specialty construction and expounds in detail how to use big data and artificial intelligence technology to design the content, implement path planning and build the evaluation system of micro-specialty construction. This micro-major trains compound talents who have mastered medicine, engineering and other fields, and have big data analysis and artificial intelligence applications, so as to provide safer, more convenient, and personalized aged care services and provide strong support for the innovation and development of the health care industry.

Advanced Custom Interconnects: A Paradigm Shift in High-Performance Computing Data Transfer Efficiency

Recent advancements in high-performance computing (HPC) systems have highlighted the critical role of custom interconnects in achieving optimal system performance and efficiency. This article comprehensively analyzes modern custom interconnect architectures, focusing on their capacity to enhance data transfer speeds while maintaining power efficiency. The article examines the integration mechanisms for heterogeneous computing elements, including CPUs, GPUs, and specialized accelerators, and evaluates their impact on system-wide performance. The article explores advanced routing algorithms and coherence protocols that enable seamless communication across multiple processing elements while ensuring data consistency. The findings demonstrate that custom interconnects significantly improve system throughput and reduce latency in large-scale HPC deployments. Furthermore, the article analyzes the implications of these advancements for cloud computing, artificial intelligence, and big data analytics applications. This article contributes to the understanding of next-generation computing infrastructure by highlighting the crucial relationship between interconnect design and overall system performance while addressing the escalating demands of modern computational workloads.

A functional data analysis approach for modelling frequency‐modulated tonal sounds in animal communication

Abstract Frequency modulation (FM) is believed to play a major role in encoding information in tonal vocal communication. However, most studies aimed at investigating acoustic variability rely on the manual measurement of acoustic parameters, and the implementation of multivariate techniques, which represent only partially sound FM. Thus, innovative modelling approaches able to capture local FM dynamics are needed for tonal sounds modelling. These kinds of methodological developments in bioacoustics might be key for further understanding animal behaviour and ecology. We propose the application of a functional data analysis (FDA) approach to model extracted FM patterns, which entails the transformation of acoustic signals into continuous functions. We describe a Raven‐to‐R FDA workflow for modelling tonal sounds and we highlight two of its potential applications for classification aims and FM analysis in relation to behavioural, social, and environmental factors. For illustrative purposes, the approach performance was tested on FM patterns (contours) of signature whistles emitted by bottlenose dolphins (Tursiops truncatus), to investigate their acoustic variability and potential information content. Our results show that tonal sounds are inherently of functional nature and can be treated as such for FM analysis. We found that building the FDA sound curves using 25 B‐spline basis functions was the most appropriate setting for the functionalisation of dolphin whistles. Our approach was able to accurately reconstruct the FM patterns of the modelled tonal sounds and even to capture their fine‐scale modulations, with a precision of 80%. Functional clustering showed an accuracy up to 89% for short whistles. Estimated functional coefficients of the regression model suggested that different contextual factors have a significant effect on the FM patterns of SWs. The adoption of an FDA approach appears to be quite promising for the study of frequency‐modulated tonal sounds, both for classification aims and in‐depth FM analysis. The limitations and advantages of the approach were discussed. We developed and tested a new methodology able to fill knowledge gaps in animal communication, and we anticipate it will lead to advances in the ethological and ecological understanding of wild animal populations.

Data-driven School Management: An Analysis of Pathways to Enhancing Student Academic Performance and Mental Health

The rapid growth of data analytics in recent years has opened up new opportunities for organizations of all kinds to make the most of large amounts of data to drive decision making and optimize operations. This paper focuses on the application of data analysis in improving student management in educational institutions, and analyzes a variety of lifestyle and behavioral variables such as students' study time, extracurricular activities, sleep duration, and social behavior. By analyzing the impact of these factors on academic performance and stress levels, this study highlights the importance of data-driven approaches in shaping education policy. The study uses statistical tools and regression analysis to identify key patterns and correlations and provide actionable recommendations for schools to enhance student academic performance and effectively manage mental health. This study aims to show how data analytics can be applied in education to help schools create more personalized, efficient and supportive learning environments. The findings highlight the importance of balancing academic demands with student well-being and suggest interventions that schools can take to optimize academic outcomes and overall student health.

Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial Intelligence

Enhancing Energy Efficiency in Photovoltaic Systems through Smart Technology Integration: Innovations and Future Perspectives

Photovoltaic (PV) technology, which converts solar radiation into electricity, has become a key player in the global transition to clean energy. As demand for renewable energy rises, innovations in smart artificial intelligence (AI), the Internet of Things (IoT), and big data analytics are being utilized to enhance the efficiency and reliability of PV systems. The integration of these technologies into PV systems is explored in this review, focusing on how they enhance fault detection, real-time monitoring, and energy optimization. It discusses the role of AI in improving system design and performance, real-time applications leveraging IoT control and data management, and the application of big data analytics for predictive maintenance and energy forecasting. Additionally, the review addresses the environmental and economic benefits of smart PV systems, highlighting their potential for reducing carbon emissions, stabilize energy grids, and provide long-term financial savings. Despite the technological and economic challenges, smart PV systems are poised to play a critical role in future global energy infrastructures by contributing to sustainable development goals and optimizing renewable energy production.

UnDRground Tubes: Exploring Spatial Data With Multidimensional Projections and Set Visualization.

In various scientific and industrial domains, analyzing multivariate spatial data, i.e., vectors associated with spatial locations, is common practice. To analyze those datasets, analysts may turn to methods such as Spatial Blind Source Separation (SBSS). Designed explicitly for spatial data analysis, SBSS finds latent components in the dataset and is superior to popular non-spatial methods, like PCA. However, when analysts try different tuning parameter settings, the amount of latent components complicates analytical tasks. Based on our years-long collaboration with SBSS researchers, we propose a visualization approach to tackle this challenge. The main component is UnDRground Tubes (UT), a general-purpose idiom combining ideas from set visualization and multidimensional projections. We describe the UT visualization pipeline and integrate UT into an interactive multiple-view system. We demonstrate its effectiveness through interviews with SBSS experts, a qualitative evaluation with visualization experts, and computational experiments. SBSS experts were excited about our approach. They saw many benefits for their work and potential applications for geostatistical data analysis more generally. UT was also well received by visualization experts. Our benchmarks show that UT projections and its heuristics are appropriate.

Predicting Academic Success and Identifying At-Risk Students: A Systematic Review of Data Analytics and Machine Learning Approaches in Higher Education Institutions

This systematic review aimed to assess the application of data analytics and machine learning techniques in Higher Education Institutions (HEIs) to predict student success and identify those at risk of academic difficulty. While previous research has explored these approaches, a comprehensive synthesis of their effectiveness and limitations has been absent. Moreover, addressing issues such as student attrition and inequitable support is crucial given the global commitment to achieving the Sustainable Development Goals (SDGs), particularly SDG 4, which prioritises inclusive and equitable quality education. A systematic literature review was conducted adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing four major databases to bridge this knowledge gap. Studies were included if they utilised data analytics and machine learning techniques to predict student success or identify at-risk students within higher education contexts. A quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed to ensure the rigour of the included studies. The review uncovered a diverse range of approaches, encompassing traditional methods such as logistic regression and support vector machines and more advanced techniques like ensemble methods and neural networks. These approaches were applied to various data sources, including administrative, learning management systems, and survey data. The findings underscored the potential of data analytics and machine learning to revolutionise higher education by facilitating the early identification of at-risk students, tailoring support services to individual needs, and optimising resource allocation. Consequently, leveraging these technologies, HEIs can make data-driven decisions to enhance student success, improve teaching and learning practices, and contribute to attaining the Sustainable Development Goals.

Climate resilient urban regeneration and SDG 11 – stakeholders’ view on pathways and digital infrastructures

ABSTRACT Urban areas grapple with escalating challenges due to climate change, rapid urbanisation, and shifting demographics. Collaborative efforts and innovative applications of big earth data analytics and digital infrastructures are imperative in tackling the issues facing cities and advancing SDG 11. Modernising urban infrastructure is essential, coupled with a focus on people-centric urban regeneration to enhance the well-being of city residents. Urban resilience, encompassing diverse social, economic, environmental, and governance facets, lies at the core of this mission. A resilient city can endure and recover from disruptions, be they natural disasters or human-made crises. Stakeholder engagement, bridging top-down and bottom-up approaches, is pivotal in fostering the use of earth data to promote sustainability and urban resilience. A literature review and on-going research underscores digital infrastructure's potential in fortifying resilient, sustainable cities. Insights from an international forum on ‘Digital Infrastructure for Climate Resilience’ held in Melbourne, Australia in 2023 inform this perspective. Big earth data analytics combined with Urban Digital Twin technologies, emerge as potent tools for urban stakeholders and decision-makers. Despite challenges, opportunities abound to leverage data and digital platforms to bolster sustainable urban development. Effective leadership, government regulations, standards, and cross-sector collaboration are essential for realising this potential.

Applicaton Of Mathematics In Big Data Analysis To Support Strategic Decision

This study aims to investigate the application of mathematical models in big data Analysis and their impact on strategic decision making in various industrial sectors. Using a quantitative approach to the survey, data was collected from 190 respondents from the technology, finance, manufacturing and healthcare sectors. The results showed that the application of mathematical models, such as predictive algorithms and machine learning, contributed significantly to improving the quality of strategic decisions. The study also identified that variables such as human resource competence and technological infrastructure moderate the relationship between big data Analysis and effective decision-making. The technology and finance sectors have proven to benefit the most from the application of math-based big data Analytics, with benefits seen in improved market prediction, risk management, and operational optimization. The findings underscore the importance of integrating mathematical models in data analysis to support data-driven decision-making in the digital age.

Effectiveness of Ethno-Nationalist Based POGIL Model to Increase the Value of Patriotism and Discipline in Children

The study aims to determine the effect of the ethno-nationalist-based Process Oriented Guided Inquiry Learning (POGIL) model to increase the value of patriotism and discipline in children through meta-analysis. The inclusion criteria in this study are a) research data obtained from Google Scholar; ERIC, ProQuest, Wiley, DOAJ and Springer, b) research must be indexed by SINTA, Scopus or journal indexing portals managed by the Ministry of Higher Education, Research and Technology equivalent to DOAJ, EBSCO and Index Copernicus, c) the topic must be relevant, d) research published in 2022-2024, e) research publications must have a value of (r), (t), or (F), f) research samples must be more than 25 children. The meta-analysis research uses the JASP application for data analysis. The results of the study concluded that there was a significant influence of the effectiveness of the Ethno-nationalist-Based Process Oriented Guided Inquiry Learning Model on the value of character of love for the homeland and discipline in children with a strong influence category (p 0.001; rRE = 1.034). Furthermore, in this study, no publication bias was detected so that it can be scientifically accounted for. These findings show that the Effectiveness Model of Process Oriented Guided Inquiry Learning Based on Ethno-nationalism is effective in increasing the Character Values of Love for the Fatherland and Discipline in Children.

Big data analytics for customer relationship management: Enhancing engagement and retention strategies

This paper examines the application of big data analytics to enhance customer relationship management (CRM) engagement and retention strategies. The primary objective is to explore how big data analytics can be leveraged to gain insights into customer behavior, preferences, and interactions, thereby improving CRM initiatives. The research methodology involves a comprehensive literature review and case study analysis of organizations that have successfully integrated big data analytics into their CRM systems. Key findings indicate that big data analytics significantly enhances the ability to personalize customer interactions, predict customer needs, and identify at-risk customers, leading to improved engagement and retention. The analysis reveals that data-driven CRM strategies enable organizations to create more targeted marketing campaigns, optimize customer service processes, and develop loyalty programs that resonate with customers. Additionally, the study highlights the importance of integrating various data sources, including social media, transactional data, and customer feedback, to gain a holistic view of the customer journey. The conclusions underscore the transformative potential of big data analytics in CRM, suggesting that its strategic implementation can lead to substantial improvements in customer satisfaction and loyalty. This research emphasizes the need for organizations to invest in advanced analytics tools and data management practices to fully capitalize on the benefits of big data analytics in their CRM efforts.

The Application and Optimization of Extensive Data Analysis in the Evaluation of the Effect of Marketing Strategies on Agricultural Machinery Enterprises

The Application and Optimization of Extensive Data Analysis in the Evaluation of the Effect of Marketing Strategies on Agricultural Machinery Enterprises

Application of Big Data Analysis in Chinese Art Song Market Research

Data reliant on fisheries are essential for successful managing fisheries. A lack of fisheries data results in clarity regarding stock status, thereby jeopardizing the financial and food safety of the consumers reliant on that stock and heightening the risk of exploitation. Recent advancements in technology for gathering, managing, and evaluating fishery-related data offer a range of options to enhance and modernize fisheries’ information systems, significantly increasing data collecting and analytical capabilities. Notwithstanding the widespread availability of pertinent consumer electronics, incorporating sophisticated information systems into fisheries oversight is still atypical rather than the norm. The article delineates the present condition, obstacles, and prospective trajectories of advanced information systems in the fishing industry to elucidate the factors hindering their acceptance. Our examination of the utilization of fishery-dependent information technologies across several worldwide fisheries sectors reveals that technological progress is stalling due to a deficiency of trust and collaboration between fishers and management. The research advocates for a solution grounded in a cross-disciplinary approach to fisheries management that underscores the necessity of working together to solve problems among participants. In our suggested structure, data suggestions are essential for efficient fishing information systems, ensuring fishermen and managers acquire, access, and profit from data related to fishing while striving towards a shared objective. An innovative strategy for fisheries information systems will enhance data protection, precision, and settlement while minimizing costs and facilitating adaptive, adaptable, near real-time managing decisions to optimize fisheries performance.

Scalability and Efficiency of Deep Learning Models on High-Performance Computing Clusters: Bibliometric Analysis

Purpose: This systematic review identifies the main advancements, core papers, voids, and outlooks regarding utilising high-performance computing (HPC) clusters for large-scale deep learning models. Moreover, it aims to assess research trends, significant approaches, and methods to improve the effectiveness and adaptability of these models. Design/Methodology/Approach: This paper involves a systematic literature review and bibliometric analysis of published articles from 2018-2023. To limit the results, some specific keywords have been introduced: Web of Science, deep learning efficiency, scalability, and HPC clusters. Studies were screened according to the PRISMA flowchart, covering 364 articles, 19 of which were included in the systematic review based on further criteria. Findings: The bibliometric analysis revealed that the most globally cited articles were from IEEE Transactions on Emerging Topics in Computing and Joule. However, the most relevant sources identified were the Journal of Supercomputing, Concurrency and Computation: Practice and Experience, and IEEE Access. Researchers from the USA, China, Korea, and the UK authored the most significant contributions. Research Limitation: The study examined only works from the Web of Science database from 2018 to 2023. Practical Implication: The proposed research results contribute crucial information to enhance the effectiveness of deep predictive models in large-scale HPC environments, which are essential for enterprises adopting artificial intelligence (AI) and machine learning (ML) methodologies in colossal data analysis applications. Social Implication: Propelling deep learning models with the help of HPC clusters can create more vital AI solutions that can respond to society's needs. Originality/ Value: The novelty of the research stems from the bibliometric assessment and the question of which sources and authors in this field are the most important.

Application of Interactive Evolutionary Computing in Personalized Interior Design

Design is a complex and clever process with multiple concepts attempting to explain it. To validate ideas and quantitatively characterize the design process, design experiments, and data analysis are required and predictable. Applying data analysis in design experiments can be challenging, and data are often underutilized. In this study, we develop an experimentation Interactive Evolutionary Computing Design (IECD) model using Upgraded Particle Swarm Optimization (UPSO) within the personalized interior design to iteratively analyze the new color combinations based on 116 designer’s preferences. The study proposed data-infused research into interior design via experimental techniques and the proposal system is evaluated, with additional feedback collected from experimented 116 designers, and data visualization analysis using Color Histogram Analysis and Auto encoders for data’s structure and patterns of the data, thus enhancing user preferences. A proposal system is developed using the collected data to suggest design options that align with individual user preferences and then integrated into the IECD framework to generate personalized design solutions that are more efficient and intelligent. The results are the effectiveness of data analysis and visualization in revealing design patterns and the impact of the data-infused IECD system to enhance the personalization and efficiency of interior design solutions. The IECD system enhances interior design efficiency and personalization through a proposal system, revealing design patterns, and aligning options with individual user preferences.

Industry 4.0 techniques applied to the improvement of the cutting process of Lithium-Ion battery terminals

The transition to Industry 4.0 has brought significant advancements in production, especially in the manufacturing of lithium-ion batteries, through the application of Artificial Intelligence (AI). This study focuses on the development of an intelligent system to improve the cutting of battery terminals, using AI algorithms to optimize precision and reduce defects. The main challenge is the imprecision in the cutting process, which results in a high rate of defective products and increases production costs due to material waste. The application of machine learning and real-time data analysis enabled automatic adjustments in the cutting process, creating a flexible and adaptable manufacturing environment. As a result, there was a significant reduction in the defect rate of the batteries, along with an increase in the quality and uniformity of the terminals. These efficiency gains demonstrate the potential of AI to improve precision and reduce costs while meeting quality requirements. The integration of AI in the cutting process not only improves quality and sustainability but also opens up new possibilities for other applications in Industry 4.0. The study demonstrates that technological innovations in flexible manufacturing processes can generate a significant competitive advantage for the renewable energy and storage sector.

Research on the Integration of Data Statistics and Analysis in the Training of Private Equity Talents

The private equity industry is in its early stages of development and has huge potential for future development. In the specific training of private equity talents in schools, we have encountered many difficulties. The first thing we face is the formulation of a private equity training program. The private equity direction of our school's finance is set up to meet the huge demand of the economic society for private equity talents. This article will focus on the application of data statistics and analysis in the training of private equity talents.

Optimization and Practice of Airline Ticket Pricing Strategy: A Comprehensive Analysis Based on Market Demand, Competitive Environment and Passenger Behavior

The optimization and practice of airline ticket pricing strategy is a complex and critical process, which needs to consider many aspects such as market demand, competitive environment and passenger behavior. This paper aims to analyze how these factors affect ticket pricing and explore how airlines can optimize their pricing strategies in practice. In practice, airlines continue to optimize pricing strategies, through dynamic pricing, class management, promotional activities and other means to increase ticket sales and profits. At the same time, airlines also need to strengthen data analysis and technology application to improve the accuracy and flexibility of pricing to better adapt to changes in market demand and passenger behavior. The optimization and practice of airline ticket pricing strategy is a continuous process, which needs to consider market demand, competitive environment and passenger behavior.