Data Analysis Applications Research Articles

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

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.

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.

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.

Application of Big Data Analytics to Determine Traffic Congestion

This study focused on the application of big data analytics to estimate roadway congestion. The implementation of big data allows a convenient approach of data collection without any field or observer biasness. It also relieves the restriction of a limited sample. Highway Capacity Manual 2000 suggested considering travel speed as the most appropriate parameter for describing traffic congestion. Therefore, this research employs a speed performance index with the concept of big data analytics to describe traffic congestion of road. This research estimates speed performance index for different sections of road. The data was gathered for three main roads of Karachi i.e. Rashid Minhas Road, Shahra e Faisal Road and Main Korangi Road. The major achievement of this study project is to propose a novel approach to calculate the speed of a vehicle without field measurements. The data is collected through Smartphone with the aid of an application available online namely My Track. The data that was utilized for this project mainly comprises of GPS Exchange Format (GPX) routes that are converted into Extensible Markup Language (XML) to run the developed script. The script was able to determine the status of congestion on a variety of highways with the help of speed performance index. The same script can be used by administrators and other transportation service providers for estimating the congestion on a real-time basis. The methods and procedures from this study would aid in the transportation planning process, especially for route selection of individuals as well as services.

Development of the Physics Practicum Apparatus based on Microcontroller: A Prototype Constructed from Misconceptions of Basic Kinematics Concepts

Critical thinking skills are fundamental in 21st-century teaching. This study aims to identify trends in research on critical thinking skills in physics in Indonesia, as reported in journal articles published between 2016 and 2022. This is meta-analysis research using the Prisma model. There were 36 articles that met the selection criteria. We performed the data analysis manually and used VOSviewer software. The indicators used for data analysis include the year, type of research, research subject, topic, treatment, data collection instrument, and the application of data analysis in each article. The analysis of article reviews reveals that the research trend for critical thinking skills in physics in Indonesia has increased, with more publications expected in 2020. Temperature and heat are the physics topics that have become the focus of research. High school students in grade X are the most frequently selected research subjects. Research on physics education typically employs quantitative methods, employs a quasi-experimental design, and utilizes a test as a data collection instrument. The most common method of data analysis in research is the t-test.

Applications of Entropy in Data Analysis and Machine Learning: A Review.

Since its origin in the thermodynamics of the 19th century, the concept of entropy has also permeated other fields of physics and mathematics, such as Classical and Quantum Statistical Mechanics, Information Theory, Probability Theory, Ergodic Theory and the Theory of Dynamical Systems. Specifically, we are referring to the classical entropies: the Boltzmann-Gibbs, von Neumann, Shannon, Kolmogorov-Sinai and topological entropies. In addition to their common name, which is historically justified (as we briefly describe in this review), another commonality of the classical entropies is the important role that they have played and are still playing in the theory and applications of their respective fields and beyond. Therefore, it is not surprising that, in the course of time, many other instances of the overarching concept of entropy have been proposed, most of them tailored to specific purposes. Following the current usage, we will refer to all of them, whether classical or new, simply as entropies. In particular, the subject of this review is their applications in data analysis and machine learning. The reason for these particular applications is that entropies are very well suited to characterize probability mass distributions, typically generated by finite-state processes or symbolized signals. Therefore, we will focus on entropies defined as positive functionals on probability mass distributions and provide an axiomatic characterization that goes back to Shannon and Khinchin. Given the plethora of entropies in the literature, we have selected a representative group, including the classical ones. The applications summarized in this review nicely illustrate the power and versatility of entropy in data analysis and machine learning.

Leveraging Big Data Analytics for Enhanced Cybersecurity: A Comprehensive Analysis of Threat Detection, Incident Response, and SIEM Systems

This article comprehensively analyzes big data analytics applications in cybersecurity, focusing on threat detection, incident response, and Security Information and Event Management (SIEM) systems. The article explores how organizations leverage big data analytics to enhance their cybersecurity posture through advanced threat detection mechanisms, improved incident response capabilities, and sophisticated SIEM implementations. The article examines various aspects of modern cybersecurity systems, including anomaly detection, predictive analytics, real-time monitoring architectures, and root cause analysis frameworks. Through detailed case studies of major platforms, including Google Security Analytics, IBM QRadar, and Splunk, the article provides insights into practical implementations and their impact on organizational security. The article also addresses emerging technologies such as quantum computing and their implications for future cybersecurity frameworks. By analyzing implementation guidelines, best practices, and research opportunities, this article offers valuable insights for organizations seeking to enhance their cybersecurity capabilities through big data analytics while providing a framework for future developments in this rapidly evolving field.

DANSEN: Database Acceleration on Native Computational Storage by Exploiting NDP

This article introduces DANSEN , the hardware accelerator component for neoDBMS, a full-stack computational storage system designed to manage on-device execution of database queries/transactions as a Near-Data Processing (NDP)-operation. The proposed system enables Database Management Systems (DBMS) to offload NDP-operations to the storage while maintaining control over data through a native storage interface . DANSEN provides an NDP-engine that enables DBMS to perform both low-level database tasks, such as performing database administration, as well as high-level tasks like executing SQL, on the smart storage device while observing the DBMS concurrency control. Furthermore, DANSEN enables the incorporation of custom accelerators as an NDP-operation (e.g., to perform hardware-accelerated ML inference directly on the stored data). We built the DANSEN storage prototype and interface on an UltraScale+HBM FPGA and fully integrated it with PostgreSQL 12. Experimental results demonstrate that the proposed NDP approach outperforms software-only PostgreSQL using a fast off-the-shelf NVMe drive and significantly improves the end-to-end execution time of an aggregation operation (similar to Q6 from CH-benCHmark, 150 million records) by ≈ 10.6×. The versatility of the proposed approach is also validated by integrating a compute-intensive data analytics application with multi-row results, outperforming PostgreSQL by ≈ 1.5×.

INFLUENCE OF ANOMALOUS VALUES ON THE ACCURACY OF FORECASTING LOSSES IN DISTRIBUTION NETWORKS

The relevance of the study is determined by modern trends in managing the operation modes of distribution electrical networks using Smart Grid technologies, as well as the need to reduce the costs incurred by distribution system operators for purchasing electricity. Accurate load forecasting results at network nodes for different forecasting horizons are crucial for this purpose. Sudden changes in network topology can increase the errors in loss forecasts as a single time series, negatively impacting network management efficiency and increasing the costs of electricity procurement to cover losses. The study proposes using forecasting methods based on artificial neural networks for the calculation and prediction of electricity losses, along with a comparison of these methods. The calculations were performed using data from one of Ukraine's distribution system operators, and the test electrical network was adapted based on the CIGRE scheme for modeling electricity losses. Since the nodal load data contained gaps and anomalies, a two-step data analysis algorithm was employed using the DBSCAN clustering method for detection and correction. As a result of loss calculations based on cleaned data, the error was reduced threefold compared to calculations based on load factors. Applying data analysis methods and forecasting methods based on artificial neural networks significantly improves the accuracy of loss calculations and minimizes errors.