Analytical Field Research Articles

STRATEGIES FOR LEVERAGING BIG DATA AND ANALYTICS FOR BUSINESS DEVELOPMENT: A COMPREHENSIVE REVIEW ACROSS SECTORS

In today's data-driven world, the ability to effectively leverage big data and analytics has become a key driver of business development across sectors. This comprehensive review explores strategies for leveraging big data and analytics to drive business development, focusing on key trends, challenges, and best practices. The review begins by highlighting the importance of big data and analytics in enabling companies to gain actionable insights from vast amounts of data. It then examines various strategies for leveraging big data and analytics, including data collection, processing, analysis, and visualization. Key trends in the field of big data and analytics are discussed, such as the increasing use of artificial intelligence and machine learning to automate data analysis processes. The review also addresses challenges associated with big data and analytics, such as data privacy and security concerns, and offers solutions to overcome these challenges. Best practices for leveraging big data and analytics for business development are outlined, including the importance of data quality, governance, and collaboration across departments. Case studies from various sectors, such as healthcare, finance, and retail, are presented to illustrate successful implementations of big data and analytics strategies. In conclusion, the review emphasizes the importance of leveraging big data and analytics to drive business development in today's competitive landscape. It highlights the need for companies to adopt a strategic approach to data management and analytics to unlock the full potential of their data and gain a competitive edge in their respective industries.
 Keywords: Strategies, Big Data, Analytics, Business Development: Leveraging.

The linear term of the Poincaré map at singularities of planar vector fields

The aim of this work is to give conditions on the parameters of a family of analytic planar vector fields with a fixed Newton diagram and a monodromic singularity in order to guarantee that the coefficient (generalized first Lyapunov constant) of the linear part of the Poincaré map has an explicit formula that depends only on the Newton diagram. As far as we know, all the monodromic classes appearing in the literature satisfy that formula.

Veri Madenciliğinde Kümeleme Teknikleri: Yöntemler, Zorluklar ve Uygulamalar Üzerine Bir Araştırma

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.

Electronic documents as resources for sociological research on the level of security of financial and legal relations

Electronic documents, such as contracts, court decisions, financial reports, etc., can contain valuable information about social factors that affect the security of financial and legal relations. Analysis of these documents helps to identify the interrelationships between public trust, power, technology, and other social phenomena in the context of their impact on the security of individuals and society as a whole. The purpose of the article is to determine the possibilities of using electronic documents as resources for sociological research to assess the level of security of financial and legal relations. The study uses historical and logical methods to highlight the current state of affairs in the field of digital data analytics, and the methods of comparison and generalization form the basis for classifying risk indicators available in the texts of electronic documents. It is indicated that in the context of the spread of digital interaction between people, understanding the sociological aspects of their behaviour reflected in electronic documents becomes key to assessing the risks of illegal financial transactions. The article substantiates the important role of electronic documents as a valuable resource for sociological research. The study is based on an interdisciplinary approach, highlighting the possibilities of integrating sociological analysis, network research, linguistic pattern recognition and data mining methods to obtain meaningful information from large amounts of electronic documents. The main results of the study include the classification of indicators of illicit financial transaction behaviour present in the texts of electronic documents. The study highlights the practical value of using electronic documents in sociological research, offering practical guidance for government officials, lawyers, and analysts to improve risk assessment and develop security measures. The materials systematized in this study demonstrate the potential of open-source intelligence (OSINT) and advanced data analytics to build meaningful sociological models using electronic documents, highlighting their importance in addressing contemporary security analytical challenges

Integration of a facile sustainable resonance Rayleigh scattering switchable-based system for feasible determination of centrophenoxine, a nootropic and antioxidant agent; application to crude materials and dosage forms

The proposed research adheres to a certain methodology to ensure that the technique used for analyzing the centrophenoxine drug is sustainable and green. It is important to highlight that several tools that have been recently developed were utilized as potential indicators of environmental sustainability and applicability. The present research presents a novel and entirely innovative method utilizing ultrasensitive spectrofluorimetry for the detection of centrophenoxine (CPX) drug. The employed methodology in this study involved the utilization of one-step, one-pot, and direct spectrofluorimetric technique, which was found to be both efficient and environmentally sustainable in the validation and assessment of the drug. Simply, when CPX and erythrosine B reagent were combined in an acidic environment, the highly resonance Rayleigh scattering product was immediately produced. The sensitivity limits were observed to be within the range of 15–47 ng mL−1, whereas the linearity was assessed to be in the range of 50–2000 ng mL−1. The optimal settings for all modifiable parameters of the system were ascertained through an analysis of centrophenoxine-erythrosine B complexes. Moreover, the system demonstrated compliance with International Council for Harmonization (ICH) specifications without encountering any issues. The suggested process was then rated on different recent environmental safety measuring metrics to see how good it was for the environment. Fortunately, the WAC standards that combine ecological and functional elements utilizing the Green/Red/Blue (RGB 12) design also acclaimed the current analytical technique as a white one. Additionally, a new applicability evaluation tool (BAGI) was employed to estimate the practicability of the planned method in the analytical chemistry field.

The Viability of Topic Modeling to Identify Participant Motivations for Enrolling in Online Professional Development

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants’ motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

Predictive analytics in climate finance: Assessing risks and opportunities for investors

Predictive analytics is increasingly recognized as a pivotal tool in climate finance, offering investors invaluable insights into both the risks posed by climate change and the opportunities for sustainable investment. This Review delves into the burgeoning field of predictive analytics within climate finance, emphasizing its significance in aiding investors to navigate the multifaceted landscape of climate-related risks and opportunities. By leveraging advanced data analytics techniques, predictive analytics empowers investors to anticipate and mitigate climate-related risks, ranging from physical risks such as extreme weather events and sea-level rise to transition risks associated with regulatory changes and technological shifts. Moreover, predictive analytics enables investors to identify emerging opportunities in sectors poised for sustainable growth, such as renewable energy, clean technology, and climate resilient infrastructure. This Review also sheds light on the methodologies and data sources utilized in predictive analytics for climate finance, encompassing climate models, satellite imagery, socioeconomic indicators, and financial data. Through the analysis of historical trends and future projections, predictive analytics provides investors with actionable insights to inform their investment decisions and align their portfolios with climate-related goals and mandates. Despite its potential benefits, the adoption of predictive analytics in climate finance is not without challenges. This Review examines the hurdles associated with data quality, model uncertainty, regulatory complexities, and the integration of climate-related factors into financial decision-making processes. Addressing these challenges necessitates interdisciplinary collaboration, robust risk assessment frameworks, and ongoing innovation in predictive analytics methodologies. In conclusion, this Review underscores the critical role of predictive analytics in climate finance and its transformative potential in enhancing the resilience and sustainability of investment portfolios. By harnessing the power of data-driven insights, investors can proactively manage climate-related risks, capitalize on sustainable investment opportunities, and contribute to the transition towards a low-carbon economy. As climate change continues to exert profound impacts on financial markets, the integration of predictive analytics represents a strategic imperative for investors seeking to navigate the evolving landscape of climate finance effectively.

The Influence of AI: The Revolutionary Effects of Artificial Intelligence in Healthcare Sector

The application of artificial intelligence (AI) in healthcare is growing as it becomes more prevalent in modern business and everyday life. It is frequently regarded as a significant technological advancement in the present period. In recent times, the fields of artificial intelligence (AI) and big data analytics have been utilised in the domain of mobile health (m-health) to establish a highly efficient healthcare system. Modern medical research utilises diverse and poorly understood data, including electronic health records (EHRs), medical imaging, and complex language that is widely unorganised. The growth of mobile applications, together with healthcare systems, is a significant factor leading to the presence of disorganised and unstructured datasets. The enhanced accessibility of diverse datasets and advanced computer techniques like machine learning can enable researchers to usher in a new era of highly efficient genetic therapy. This review paper has clarified the role of machine learning algorithms in healthcare systems.

A new xG model for football analytics

The aim of this exploratory study is to refine and improve, in terms of prediction performance, the expected goal (xG) model, one of the emerging tools in the field of football analytics. With this final goal, we merged data from different sources: tracking data, match event data and some players’ performance composite indicators. Using an original sample of match data relying on the 2019/2020 season of the Italian Serie A, composed of 660 shots, one outcome (i.e. the GOAL) and 22 regressors, a supervised machine learning approach (logistic regression model with imbalanced training sample adjustment) was applied to different scenarios for sample balanced techniques. Results are interesting in terms of sensitivity and F1 metrics, compared with a benchmark (Understat). Other results concerning the classic imbalance framework significantly outperform the benchmark in terms of the AUC metric. In addition, some performance composite indicators and one original tracking variable are significant for the classification model, contributing to increasing the goal prediction probability compared with the benchmark.

Exploring Pattern Mining with FCM Algorithm for Predicting Female Athlete Behaviour in Sports Analytics

In the changing field of sports analytics, examining and predicting athlete behaviour is crucial for improving performance and reaching the best results. As data analysis becomes more important in sports, using pattern mining methods is critical to discovering meaningful patterns and trends in the large datasets used in sports analysis. This study proposes a Performance Optimization Framework for Female Athletes (POFFA) that explores the field of sports analytics to examine how pattern mining methods, explicitly using the Flexible Fuzzy C-Means (FFCM) algorithm, can help predict female athlete behaviour. This study aims to offer detailed insights into behavioural patterns that can help improve coaching techniques, training schedules, and performance improvement procedures by concentrating on female athletes. The study intends to provide a deep understanding of female athlete behaviour and game dynamics using the FCM approach, known for its effectiveness in dealing with complex, sparse, multi-objective optimization difficulties. The results of this study can enhance the field of sports analysis by providing detailed insights into the behavioural patterns displayed by female players in many sports categories. By combining pattern mining methods with the FCM algorithm, this study introduces a thorough process for examining and predicting athlete behaviour in sports analytics. In the end, the results of this study have practical significance for coaches, trainers, and sports professionals, offering helpful advice for improving the performance of female athletes in competitive sports settings. By using data-driven insights from pattern mining analyses, individuals in the sports business can improve their plans and interventions to enhance female athletes' athletic potential and overall performance results.

Advancing Solar Magnetic Field Extrapolations through Multiheight Magnetic Field Measurements

Nonlinear force-free extrapolations are a common approach to estimate the 3D topology of coronal magnetic fields based on photospheric vector magnetograms. The force-free assumption is a valid approximation at coronal heights, but for the dense plasma conditions in the lower atmosphere, this assumption is not satisfied. In this study, we utilize multiheight magnetic field measurements in combination with physics-informed neural networks to advance solar magnetic field extrapolations. We include a flexible height-mapping, which allows us to account for the different formation heights of the observed magnetic field measurements. The comparison to analytical and simulated magnetic fields demonstrates that including chromospheric magnetic field measurements leads to a significant improvement of our magnetic field extrapolations. We also apply our method to chromospheric line-of-sight magnetograms from the Vector Spectromagnetograph (VSM) on the Synoptic Optical Long-term Investigations of the Sun (SOLIS) observatory, in combination with photospheric vector magnetograms from the Helioseismic Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The comparison to observations in extreme-ultraviolet wavelengths shows that the additional chromospheric information leads to a better agreement with the observed coronal structures. In addition, our method intrinsically provides an estimate of the corrugation of the observed magnetograms. With this new approach, we make efficient use of multiheight magnetic field measurements and advance the realism of coronal magnetic field simulations.

Soccer match outcome prediction with random forest and gradient boosting models

In order to accurately predict the results of soccer matches, this study introduces Machine Learning (ML) techniques in joint Random Forest (RF) and Gradient Boosting (GB) models. In order to forecast the results of the next World Cup, a model has been trained using past information from prior tournaments. The proposed model is evaluated using multiple performance criteria including precision and accuracy. The RF approach outperforms the GB approach in terms of both accuracy and precision, as concluded after the experiment. The most important features for predicting the outcome of football games are identified using feature importance scores. Football enthusiasts and analysts can use the proposed model to predict the outcome of football games with high accuracy. The implications of these findings for football teams are practical as they provide valuable insights for improving team performance and increasing their chances of winning the World Cup. By identifying the most important features for predicting the outcome of football games, teams can focus their efforts on improving these areas, increasing their chances of success. Football teams and football analysts can benefit from accurate predictions, which are enabled by machine learning techniques such as GB and RFs. Overall, this study presents a promising approach to predicting the outcome of football games, with practical implications for the field of sports analytics.

A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites

For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.

Exploring the relationship between LMS interactions and academic performance: A Learning Cycle approach

Research on the relationship between the digital traces of students in Learning Management Systems (LMS) and their academic performance has traditionally been an area of interest in the field of learning analytics. Aiming at achieving high interpretability and generalizability, this study reviews past research, defines a new categorization scheme for interactions in LMS and investigates the relationships between clickstream data of students’ activity and course performance, measured as final grade. The results of the multiple regression analysis of Moodle log data collected from three courses of diverse nature using various classifications of interactions suggest that the new categorization, the CILC (Classification of Interactions based on the Learning Cycle), improves the explanatory power when compared to previous classifications. The analysis suggests that the predictive ability of the models could depend on the delivery mode, with predictions improving as the delivery mode transitions from face-to-face learning to online learning. This finding highlights the need for context-specific considerations about the learning process. Compared to previous research, the analysis also reveals nuances that suggest that the relationships may depend on the instructional design. Finally, the findings also seem to support the notion that increased data quantity and quality may improve predictive models. In summary, the study contributes with valuable insights into the interplay between LMS interactions, course delivery mode, instructional design, and academic performance, and advocates for a balanced exploration of white-box and black-box modeling approaches in learning analytics research.

QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics.
 Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.

LEVERAGING BIG DATA FOR PERSONALIZED MARKETING CAMPAIGNS: A REVIEW

The burgeoning field of big data analytics has revolutionized the landscape of marketing, offering unprecedented opportunities for personalized marketing campaigns. This review aims to synthesize the current state of knowledge on leveraging big data for personalized marketing, elucidating the objectives, methodologies, key findings, and conclusions drawn from recent research in this domain. The primary objective of this review is to explore how big data analytics can be effectively utilized to tailor marketing strategies to individual consumer preferences, behaviors, and patterns. Methodologically, the review adopts a comprehensive approach, examining a wide range of studies that employ various big data tools and techniques, including machine learning algorithms, data mining, and predictive analytics, in the context of personalized marketing. Key findings indicate that big data analytics significantly enhances the ability of marketers to understand and predict consumer behavior, leading to more effective targeting and segmentation strategies. The integration of big data has shown to improve customer engagement, satisfaction, and loyalty by delivering more relevant and timely marketing messages. However, challenges such as data privacy concerns, the need for advanced analytical skills, and the potential for data inaccuracies are also highlighted. In conclusion, while big data presents substantial opportunities for personalizing marketing campaigns, its effective implementation requires careful consideration of ethical implications, investment in technological infrastructure, and ongoing skill development. Future research directions include exploring the impact of emerging technologies like artificial intelligence and the Internet of Things (IoT) on personalized marketing, and developing frameworks for ethical data usage in marketing practices. This review underscores the transformative potential of big data in reshaping personalized marketing strategies, offering valuable insights for both practitioners and researchers in the field.
 Keywords: Big Data, Marketing Strategies, Consumer Behavior, Data Analytics, Personalized Marketing, Market Segmentation, Privacy Concerns, Ethical Challenges, Digital Transformation, Artificial Intelligence (AI).

Improving academic performance predictions with dual graph neural networks

AbstractAcademic performance is a crucial issue in the field of Online learning analytics. While deep learning-based models have made significant progress in the era of big data, many of these methods need help to capture the complex relationships present in online learning activities and student attributes, which are essential for improving prediction accuracy. We present a novel model for predicting academic performance in this paper. This model harnesses the power of dual graph neural networks to effectively utilize both the structural information derived from interaction activities and the attribute feature spaces of students. The proposed model uses an interaction-based graph neural network module to learn local academic performance representations from online interaction activities and an attribute-based graph neural network to learn global academic performance representations from attribute features of all students using dynamic graph convolution operations. The learned representations from local and global levels are combined in a local-to-global representation learning module to generate predicted academic performances. The empirical study results demonstrate that the proposed model significantly outperforms existing methods. Notably, the proposed model achieves an accuracy of 83.96% for predicting students who pass or fail and an accuracy of 90.18% for predicting students who pass or withdraw on a widely recognized public dataset. The ablation studies confirm the effectiveness and superiority of the proposed techniques.

Novel analytic EM field analysis and calculation formula for a ground resistance of a vertical ground rod in two-layer earth. part I: Theoretical analysis

The accuracy of calculating the ground resistance for vertical ground rods within extensive ground grids holds paramount significance for both personnel safety and the overall safety of the grid itself. This paper introduces a unique and innovative approach by presenting and analysing a novel frequency-dependent formula, with comprehensive analytical mathematical expressions. This formula is designed to accurately compute the ground impedance of a vertical ground rod buried in soil with two layers, penetrating both layers. Additionally, it offers a thorough electromagnetic field analysis associated with this setup and accounts for the impact of fault types, specifically considering the frequency of the current.

Analytical fields of ultrashort radially polarized laser beams with spatial chirp

We find the analytical electromagnetic fields, both paraxial and with non-paraxial corrections, of an ultrashort radially polarized pulse-beam that has spatial chirp. This represents a powerful description of light that has a combination of both vector polarization and space-time structure, and it results in a novel evolution of the fields. The non-paraxial corrections allow for the application of the field solutions to tightly focused scenarios, whereby we can validate our solution via charged particle trajectories under the influence of such a pulse with high field strength.

Novel Approaches to Multi-Disease Prognosis: An All-Encompassing Investigation to Improve Medical Results

This thorough review paper uses machine and deep learning approaches to synthesize and analyze recent advances in multi-disease prediction. The study explores the methods used, datasets used, and findings produced in the context of forecasting many diseases like diabetes, heart disease, renal disease, Alzheimer's, and cancer. It does this by drawing on insights from 10 different research papers. Modern deep learning models like Artificial Neural Networks and Convolutional Neural Networks, as well as more conventional algorithms like Random Forest and Support Vector Machines, are examined. The paper addresses issues such model stability, interpretability, and data privacy while highlighting the crucial role that efficient prediction models play in the healthcare industry. It offers a comparative study of performance measures from various research, emphasizing the disparate accuracy levels attained by various algorithms. Additionally, the research finds common themes used in other investigations, including feature selection, data preprocessing, and hybrid model integration. The importance of efficiency and adaptability in multi-disease prediction models is emphasized, especially in addressing drawbacks of current methods. Together, the findings deepen our understanding of the dynamic field of multi-disease prediction and open up new avenues for future investigation. These avenues include the incorporation of the Internet of Things (IoT), the exploration of multi-feature inputs, and the creation of more potent prediction models. For those working in the fields of predictive analytics and healthcare, researchers, practitioners, and policymakers, this review is an invaluable resource