Data Analysis Capabilities Research Articles

Applications of generative artificial intelligence to augment clinician's capability for medical data analysis in RStudio.

Applications of generative artificial intelligence to augment clinician's capability for medical data analysis in RStudio.

Progress on deep learning in genomics.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.

Leveraging Geo-Profiling to Address Rape as a Public Health and Criminal Epidemic in the United States

Abstract Rape, as a pervasive societal issue, transcends the boundaries of public safety, criminal justice, legal jurisprudence, and public health, casting a long shadow of fear, trauma, and profound consequences across communities. This article delves into the multifaceted nature of rape, recognizing it not solely as a criminal phenomenon but as a complex public health crisis demanding comprehensive intervention. In response to the imperative of addressing rape and serial rapists as a criminal and public health epidemic, this study introduces an innovative approach that harnesses the potential of artificial intelligence (AI) and geo-profiling. Through synthesizing criminological and epidemiological insights, this research elucidates the transformative role of geo-profiling in understanding, preventing and solving rape crimes. Geo-profiling data analytics capabilities are harnessed to dissect the intricate details, patterns and dynamics of rape incidents, offering a data-driven foundation for preventive strategies. Moreover, integrating geo-profiling techniques empowers law enforcement and public health agencies to map high-risk behaviors, areas and optimize resource allocation geographically. This holistic approach advances our understanding of rape as a public health crisis and informs targeted, evidence-based interventions to reduce its prevalence. In a contemporary landscape marked by the imperative of innovation and interdisciplinarity, this article contributes a nuanced perspective to the discourse surrounding rape. By unifying law enforcement, criminal justice, legal, and public health paradigms and harnessing geo-profiling as a powerful tool, we embark on a path to address rape comprehensively, recognizing its profound impact on individuals and society.

The Role of Logistics Innovation in Enhancing Supply Chain Efficiency: A Case Study Analysis

This paper delves into the role of logistics innovation in enhancing supply chain efficiency and demonstrates through case studies the positive impact of logistics innovation on cost control, customer service, environmental sustainability, and corporate competitiveness. The study finds that by introducing automation and information technology, companies can significantly improve the speed and accuracy of logistics operations, reduce operational costs, enhance customer satisfaction, promote environmental sustainability, and enhance market competitiveness. The research also proposes application suggestions for logistics innovation in supply chain management, including continuous investment in technological innovation, strengthening data analysis capabilities, promoting green logistics practices, strengthening customer participation, and establishing a flexible supply chain. Finally, the study points out the limitations of the research and provides suggestions for future research directions.

중소 수출 제조기업의 빅데이터 분석 역량이 공급망 회복탄력성과 공급망 성과에 미치는 영향에 관한 연구 - 기계/장비 산업을 중심으로

As the economic environment of the global market is rapidly changing, uncertainty is increasing, and technological innovation, as well as financial and human resources, are becoming important factors. In particular, small and medium-sized export manufacturing companies are building global supply chains to improve their competitive advantage, and big data analytics capabilities to collect and analyze manufacturing and import/export information have been raised as a way to solve these risks. Therefore, this study analyzed the influence of big data analytics capabilities on supply chain performance through logistics flexibility and supply chain resilience in the supply chain formation of small and medium-sized export manufacturing firms. The survey was distributed to small and medium-sized export manufacturing companies from January 3, 2024, to March 31, 2024, through face-to-face, email, mail, and fax, focusing on the list of member companies provided by the Korea International Trade Association, the Ministry of SMEs and Startups, the Smart Manufacturing Innovation Association, and the SME Technology Information Promotion Agency. More than 2,000 copies were distributed and 215 copies (21.5%) were collected, and 203 copies of the questionnaire were constructed after removing 12 non-response questionnaires. Based on the collected questionnaires, the analysis was conducted using SPSS Ver. 28.0 and the extension program Process Macro Ver. 4.2. The results of the empirical analysis of this study are as follows. First, big data analytics capabilities have a positive impact on supply chain performance. Second. big data analytics capabilities have a positive effect on supply chain performance through the mediation of logistics flexibility. Third, big data analytics capabilities have a positive effect on supply chain performance through the mediation of supply chain resilience. Fourth, big data analytics capabilities have a positive effect on supply chain performance through the dual media.

The role of big data and AI in enhancing biodiversity conservation and resource management in the USA

The rapid advancement of big data and artificial intelligence (AI) technologies has opened new frontiers in biodiversity conservation and resource management, particularly in the USA. This review paper explores the significant roles these technologies play in enhancing conservation efforts and resource management strategies. The paper begins by contextualizing the importance of biodiversity in the USA and the existing challenges in conservation practices. It then delves into the realm of big data, discussing its sources, collection methods, storage, integration, and real-world applications. Furthermore, it examines AI technologies, highlighting their capabilities in data analysis, predictive modeling, and habitat monitoring. The synergy between big data and AI is also analyzed, showcasing how their integration can lead to more informed decision-making and efficient management practices. Case studies illustrate the practical applications and benefits of these technologies. The paper concludes with a summary of key findings, recommendations for policymakers and practitioners, and suggestions for future research directions. Through this comprehensive review, the paper underscores the transformative potential of big data and AI in safeguarding biodiversity and managing natural resources effectively.

The role of artificial intelligence in revolutionizing personalized medicine: A comprehensive review of techniques and applications

This comprehensive review explores the transformative role of Artificial Intelligence (AI) in revolutionizing personalized medicine, focusing on techniques, applications, and future directions. Personalized medicine, which tailors medical interventions to individual characteristics, has significantly advanced through the integration of AI technologies. AI's capabilities in data integration, genomic and molecular data analysis, predictive modeling, and personalized treatment development are highlighted. The review also addresses the challenges and limitations of AI in personalized medicine, such as data privacy, bias, and the need for seamless clinical integration. Furthermore, it discusses future trends, including advances in AI technologies, the integration of multi-omics data, and the importance of ethical and regulatory considerations. The historical evolution of personalized medicine is traced, emphasizing key milestones that have led to current innovations. This review underscores AI's pivotal role in enhancing the precision and effectiveness of personalized healthcare, offering insights into how AI-driven approaches can shape the future of medicine.

Bibliometric Analysis to Understand Research Trends in Data-Driven Economics

The bibliometric analysis conducted in this study offers a nuanced understanding of the evolution and current state of research within data-driven domains, highlighting "data" as a central theme interconnected with diverse fields like business, smart cities, and machine learning. This research provides valuable insights into the dominant trends, collaboration patterns, and thematic priorities that shape the data-driven research landscape, underscoring the critical role of data in advancing both theoretical knowledge and practical applications across various sectors. The findings suggest significant opportunities for enhancing data analytics capabilities, developing targeted educational programs, and fostering interdisciplinary collaborations that can bridge existing gaps in the literature. However, the study also notes limitations inherent in bibliometric analyses, including potential biases toward more frequently cited or recent publications and the exclusion of works outside selected databases. Overall, this analysis not only reflects the dynamic and impactful nature of data-driven research but also guides future academic and practical endeavors in the field.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

Artificial intelligence in psychodermatology: A brief report of applications and impact in clinical practice.

This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection between mental health and skin disorders, stands to benefit from AI's advanced data analysis and pattern recognition capabilities. A literature search was conducted on PubMed and Google Scholar, spanning from 2004 to 2024, following PRISMA guidelines. Studies included demonstrated AI's effectiveness in predicting treatment outcomes for body dysmorphic disorder, identifying biomarkers in psoriasis and anxiety disorders, and refining therapeutic strategies. The review identified several studies highlighting AI's role in improving treatment outcomes and diagnostic accuracy in psychodermatology. AI was effective in predicting outcomes for body dysmorphic disorder and identifying biomarkers related to psoriasis and anxiety disorders. However, challenges such as limited dermatologist knowledge, integration difficulties, and ethical concerns regarding patient privacy were noted. AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care. Nonetheless, realizing this potential requires large-scale clinical validation, enhanced dataset diversity, and robust ethical frameworks. Future research should focus on these areas, with interdisciplinary collaboration essential for overcoming current challenges and optimizing patient care in psychodermatology.

Advancing Refugee Health Data Management: The Implementation of ICD-11 in UNRWA's Primary Care System.

The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) was one of the earliest healthcare systems globally to implement the International Classification of Diseases, Eleventh Revision (ICD-11) across its 140 clinics serving 5.9 million Palestine refugees. This paper discusses the integration of ICD-11 into UNRWA's cloud-based electronic medical record (EMR) system, identifying both the barriers and facilitators involved and analyzing trends in clinical documentation and healthcare utilization. The key challenges included data privacy provisions, integration into a coordinated care model, complex classification schema for primary care settings, frequent staff turnover, and limited data analysis capabilities. Conversely, facilitators included physician-tailored training and on-site support, system compatibility, a multidisciplinary team approach, policy support from UNRWA and the World Health Organization (WHO), and leadership commitment and effective change management. Medical officers (MOs) using ICD-11 reported greater satisfaction with the system's capabilities in managing and visualizing health information. This article contributes to the discourse on health data management in complex humanitarian settings, offering insights into the benefits and challenges of implementing advanced classification systems like ICD-11. Future research should explore longitudinal impacts and further integration with global health systems, ensuring that the advancements in classification continue to support the overarching goal of health equity and access in vulnerable and hard-to-reach populations.

An Overview of Sensor Fusion and Data Analytics in WSNs

Wireless sensor networks (WSNs) have emerged as a powerful technology for a wide range of applications, including environmental monitoring, industrial automation, and healthcare. One of the key challenges in WSNs is effectively leveraging the vast amounts of data collected by the sensor nodes to extract meaningful insights and support informed decision-making. This research paper explores the concept of sensor fusion and data analytics in the context of wireless sensor networks. We discuss the benefits and challenges of integrating data from multiple sensors, and present various techniques and algorithms for fusing sensor data and conducting advanced analytics. The paper also examines the role of machine learning and artificial intelligence in enhancing the capabilities of sensor fusion and data analytics in WSNs. Finally, we provide a discussion of the practical considerations and future research directions in this field.

The Application of Mathematics in Geophysical Image Processing and Analysis

With the widespread application of big data technology across various industries, cultivating big data analysis capabilities in accounting-related majors has become a crucial direction in higher education. This study aims to explore the construction of a practical training base for big data analysis in accounting majors in the "intelligent" era. By analyzing the current application status of big data technology in the accounting field and combining it with educational and teaching needs, a systematic and scientific training base construction plan is proposed. The research results show that building a big data analysis training base can not only enhance students' data analysis capabilities and practical skills but also provide strong support for the teaching reform of accounting majors in colleges and universities.

Leveraging Supply Chain Reaction Time: The Effects of Big Data Analytics Capabilities on Organizational Resilience Enhancement in the Auto-Parts Industry

Big data analytics capabilities (BDACs) are strategic capabilities that expedite decision-making processes, empowering organizations to mitigate the impacts of supply chain disruptions. These capabilities enhance the ability of companies to be more proactive in detecting and predicting disruptive events, increasing their resilience. This study analyzed the effects BDACs have on firms’ reaction time and the effects companies’ reaction time has on their resilience. The research model was assessed with 263 responses from a survey with professionals of auto-parts companies in Brazil. Data were analyzed with the Partial-Least-Squares—Structural Equation Modeling method. Cluster analysis techniques were also applied. This study found that BDACs reduce reaction time, which, in turn, improves firms’ resilience. We also observed greater effects in first-tier and in companies with longer Industry 4.0 journeys, opening further perspectives to investigate the complex mediations of digital readiness, reaction time, and organizational resilience performance of firms and supply chains. Our research builds upon the dynamic capabilities theory and identifies BDACs as dynamic capabilities with the potential to enhance resilience by reducing data, analytical, and decision latencies, which are recognized as core elements of the reaction time concept, which is particularly crucial during disruptive supply chain events.

When does entrepreneurial leadership enhance supply chain resilience? A three-way interaction analysis

PurposeThe research on entrepreneurial leadership (EL) and supply chain resilience (SCR) relationship is still in its exploration stage. Previous research indicates that EL is important for enhancing supply chain performance, including its resilience. However, the conditions through which EL affects SCR remain unclear. The purpose of this study is to examine the effect of EL on SCR in the context of SMEs, in addition to explore the combined moderating effects of big data analytics capability (BDAC) and environmental dynamism (ED) in the EL–SCR relationship.Design/methodology/approachHierarchical regression analysis via PROCESS Macro was used to analyze the data collected from manufacturing SMEs in India.FindingsThe findings reveal that EL positively and significantly affects SCR. Importantly, BDAC actually positively moderates the EL–SCR relationship regardless of the level of dynamism of the business environment.Originality/valueThis research expands the existing body of knowledge on EL and SCR within the context of developing countries. The findings offer useful insights for SME managers and decision-makers, who are anticipated to consider the adoption of BDAC in their firms. This has implications that with BDAC adoption, it is anticipated that SMEs will be able to take advantage of the full potential of EL to enhance their SCR regardless of the level of dynamism of the business environment.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Advancing Economic Recovery with Artificial Intelligence, Quantum Computing Technologies, and Strategic Management in Small Businesses

This abstract introduces a comprehensive approach to fostering economic recovery through the integration of cutting-edge technologies and strategic management in small businesses. In the wake of global economic challenges, the utilization of Artificial Intelligence (AI) and Quantum Computing (QC) emerges as a pivotal strategy. AI offers unparalleled capabilities in data analysis, customer engagement, and operational efficiency, empowering small businesses to adapt swiftly to market demands and optimize resource allocation. Concurrently, QC revolutionizes data processing by exponentially enhancing computational power, enabling small enterprises to tackle complex problems and innovate at unprecedented speeds. However, the effective deployment of these technologies necessitates astute strategic management. Strategic foresight and agile decision-making are crucial in harnessing AI and QC to their fullest potential, ensuring alignment with business objectives and mitigating risks. This abstract advocates for a holistic approach wherein small businesses integrate AI and QC technologies seamlessly into their operational frameworks while fostering a culture of strategic innovation. By leveraging these advancements in tandem with strategic management principles, small enterprises can not only navigate economic uncertainties but also unlock new growth opportunities, catalyzing sustainable economic recovery and long-term prosperity.

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities.

High-throughput technologies yield a broad spectrum of multi-omics datasets, which offer unparalleled insights into complex biological systems. However, effectively analyzing this diverse array of data presents challenges, considering factors such as species diversity, data types, costs, and limitations of the available tools. Herein, we present ExpOmics, a comprehensive web platform featuring 7 applications and 4 toolkits, with 28 customizable analysis functions spanning various analyses of differential expression, co-expression, Weighted Gene Co-expression Network Analysis (WGCNA), feature selection, and functional enrichment. ExpOmics allows users to upload and explore multi-omics data without organism restrictions, supporting various expression data, including genes, mRNAs, lncRNAs, miRNAs, circRNAs, piRNAs, and proteins and is compatible with diverse gene nomenclatures and expression values. Moreover, ExpOmics enables users to analyze 22427 transcriptomic datasets of 196 cancer subtypes sourced from 63 projects of The Cancer Genome Atlas Program (TCGA) to identify cancer biomarkers. The analysis results from ExpOmics are presented in high-quality graphical formats suitable for publication and are available for free download. A case study using ExpOmics identified two potential oncogenes, SERPINE1 and SLC43A1, that may regulate colorectal cancer through distinct biological processes. In summary, ExpOmics can serves as a robust platform for global researchers to explore multi-omics data, gain biological insights, and formulate testable hypotheses. ExpOmics is available at http://www.biomedical-web.com/expomics.

Research on Piano Program Programming and Creative Optimization Methods Based on Big Data Technology

This study focuses on the programming and creative optimization methods of piano tracks based on big data technology. Through in-depth analysis of the potential application of big data technology in the field of music creation, we propose an innovative framework for piano repertoire programming and creative optimization. This framework fully utilizes the data processing and analysis capabilities of big data technology to mine massive piano repertoire data, extract music elements, style features, and creative patterns. In terms of piano repertoire programming, we use big data technology to quantitatively analyze music elements and construct a data-driven programming model. This model can automatically generate piano repertoire frameworks that match specific styles, emotions, and rhythms based on user creative needs, providing inspiration and direction for creators. In terms of creative optimization, we utilize machine learning algorithms based on big data technology to intelligently evaluate and improve piano tracks. By comparing and analyzing the advantages and disadvantages of different tracks in terms of melody, harmony, structure, etc., we have discovered and extracted the common characteristics of excellent works, providing guidance for creators to optimize their work. This article will focus on the programming and control capabilities of music programming in the design and composition process of big data technology, as well as optimization methods for music creation. Provide precise services for music teaching and research, promote the prosperity and development of music, and accelerate the integration and innovative development of information technology and music education and teaching. In terms of piano repertoire programming, we utilized big data technology to quantitatively analyze music elements and constructed a data-driven programming model. This model can provide creators with rich inspiration and clear direction based on their creative intentions and specific styles. In addition, our study also considered the recognition rates of training models for different instrument types. Through training and optimizing models, we have ensured that piano program programming and creative optimization processes can achieve high recognition rates and accuracy on different instrument types, thereby further expanding the application scope of big data technology in the field of music creation.

Advanced analysis of single-molecule spectroscopic data

We present Full SMS, a multipurpose graphical user interface (GUI)-based software package for analyzing single-molecule spectroscopy (SMS) data. SMS typically delivers multiparameter data—such as fluorescence brightness, lifetime, and spectra—of molecular- or nanometer-scale particles such as single dye molecules, quantum dots, or fluorescently labeled biological macromolecules. Full SMS allows an unbiased statistical analysis of fluorescence brightness through level resolution and clustering, analysis of fluorescence lifetimes through decay fitting, as well as the calculation of second-order correlation functions and the display of fluorescence spectra and raster-scan images. Additional features include extensive data filtering options, a custom HDF5-based file format, and flexible data export options. The software is open source and written in Python but GUI based so it may be used without any programming knowledge. A multiprocess architecture was employed for computational efficiency. The software is also designed to be easily extendable to include additional import data types and analysis capabilities.