Big Technology Research Articles

Implementation of Business Intelligence for Analysis Data of Drug Sales Using Exploratory Data Analysis (EDA) and Visualization Data Using Looker Studio

This study aims to analyze pharmaceutical sales performance by leveraging big data and advanced information technology tools, providing insights for improved business strategies. Using the Exploratory Data Analysis (EDA) method, the study processes raw sales data through spreadsheet applications to identify key patterns and trends. The findings are then visualized with Looker Studio on an intelligent dashboard tailored to the needs of the marketing team. The dashboard enables quick, data-driven decision-making by displaying performance metrics and trends relevant to sales and marketing strategies. The results reveal critical insights into sales behavior, including high-performing products, regional sales variations, and temporal sales trends. The analysis equips the marketing team with actionable data to refine their strategies, prioritize product focus, and adjust marketing efforts in response to identified patterns. In conclusion, the use of EDA and data visualization provides a structured approach to big data interpretation, allowing the company to optimize business and marketing performance. The study underscores the potential of data-driven dashboards to enhance strategic planning, suggesting broader applications in various data-intensive business environments.

Research on Digital Transformation Strategy of Multinational Company Sales Based on Big Data and AI

Abstract: In the wake of the COVID-19 pandemic, the digital transformation of industries has become paramount, with multinational corporations (MNCs) recognizing the pivotal role of sales digitization in their development trajectory. This study delves into the multifaceted impact of sales digitization on MNCs, elucidating both its benefits and the formidable challenges it presents. Notably, the integration of big data and artificial intelligence (AI) has revolutionized the collection and analysis of sales data, offering MNCs unprecedented insights into consumer behavior and market dynamics. By leveraging big data and AI technologies, MNCs can optimize their operations and substantially reduce the costs associated with data collection, particularly in diverse and segmented markets. This transformative synergy plays a pivotal role across various domains, including market analysis, predictive modeling, customer relationship management, and the formulation of agile marketing strategies. Despite these advancements, research reveals that MNCs continue to grapple with significant barriers and complexities in the process of sales digitization. This article aims to bridge this research-practice gap by offering pragmatic solutions and actionable strategies tailored to address the identified challenges. By emphasizing the importance of organizational agility, talent acquisition, and strategic partnerships, MNCs can effectively navigate the complexities of sales digitization and unlock its full potential. Ultimately, this study seeks to provide a comprehensive understanding of the evolving landscape of digital sales in MNCs, culminating in a nuanced conclusion that underscores the imperative of adaptability and innovation in an increasingly digitized global economy.

Sustainable Smart Cities Planning in Conjunction with Environment Governance

In the present age, many issues have been brought to the forefront with the development of cities, hence the concept of sustainable smart cities has been introduced. Based on the methodology of big data analytics and related technologies, this paper gives the planning for sustainable smart cities from several perspectives. Starting from the analysis of urban industries and architecture, this paper analyses the distribution of urban population, presents and analyses the use of renewable energy sources in cities and the use of advanced traffic systems through several successful cases, and introduces the application of internet technology at the end. Many perspectives in this paper start with examples from real cities, thus giving evaluations as well as suggestions for optimisation. The analyses of smart cities throughout the text also deal with environmental governance, aiming to make the topic of sustainable development realisable, so that the economy and environment of the smart cities in the future can be mutually reinforcing.

Applications and analyses of ammonia slip control based on precise ammonia injection technologies guided by big data

Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

Large Language Publishing

The AI hype cycle has come for scholarly publishing. This essay argues that the industry’s feverishーif mostly aspirationalーembrace of artificial intelligence should be read as the latest installment of an ongoing campaign. Led by Elsevier, commercial publishers have, for about a decade, layered a second business on top of their legacy publishing operations. That business is to mine and process scholars’ works and behavior into prediction products, sold back to universities and research agencies. This article focuses on an offshoot of the big firms’ surveillance-publishing businesses: the post-ChatGPT imperative to profit from troves of proprietary “training data,” to make new AI products andーthe essay predictsーto license academic papers and scholars’ tracked behavior to big technology companies. The article points to the potential knowledge effects of AI models in academia: Products and models are poised to serve as knowledge arbitrators, by picking winners and losers according to what they makevisible. I also cite potential knock-on effects, including incentives for publishers to roll back open access (OA) and new restrictions on researchers’ access to the open web. The article concludes with a call for a coordinated campaign of advocacy and consciousness-raising, paired with high-quality, in-depth studies of publisher data harvestingーbuilt on the premise that another scholarly-publishing world is possible. There are many good reasons to restore custody to the academy, the essay argues. The latest is to stop our work from fueling the publishers’ AI profits.

Evaluasi Pengalaman Pengguna Pada Learning Management System Menggunakan Metode User Experience Questionnaire

The purpose of this study was to conduct a survey to assess the performance of the Learning Management System (LMS). This is done related to the need to evaluate its use by users. This study used the User Experience Questionnaire (UEQ) method regarding (1) attractiveness, (2) clarity, (3) efficiency, (4) reliability, (5) stimulation, and (6) novelty. The survey was conducted online via a Google form and had 164 participants. The benchmark results show that all criteria are above the average, but the UEQ scale for novelty is the lowest at 0.87, while attractiveness is 1.44, pragmatic quality is 1.37, and hedonic quality is 1.05. Therefore, it is suggested that the learning management system be improved through interactive learning styles, such as live discussions in LMS, integration with other technologies, updating artificial intelligence and big data-based technologies, and adapting to the metaverse, while maintaining the attractiveness and identity of the platform through design. rework color schemes, content, including engaging videos and gamification, and implement live chat with lecturers to overcome time constraints during video conferencing.

Precision emergency medicine in health care delivery and access: Framework development and research priorities.

The integration of precision emergency medicine (EM) into our conceptualization of the health care system affords the opportunity to improve health care access, delivery, and outcomes for patients. As part of the Society for Academic Emergency Medicine (SAEM) Consensus Conference, we conducted a rapid literature review to characterize the current state of knowledge pertaining to the intersection of precision EM (defined as the use of big data and technology to deliver acute care for individual patients and their communities) with health care delivery and access. We then used our findings to develop a proposed conceptual model and research agenda. We completed a rapid review of the existing literature on the utilization of big data and technology to ensure and enhance access to acute/unscheduled care for individual patients and their communities. Literature searches were conducted using Ovid MEDLINE, Embase.com, Cochrane CENTRAL via Ovid, and ClinicalTrials.gov in January 2023. Using the identified articles, we determined core domains, developed a framework to guide the conceptualization of precision EM in health care delivery and access, and used these to identify a research agenda. Of the 815 studies identified for initial screening, 60 underwent full-text review by our technical expert panel and 21 were included in the evaluation. Core domains identified included expedited/personalized prehospital care, delivery to the right level of care, personalized ED care, alternatives to ED care/post-ED care, prediction tools for system readiness, and creation of equitable systems of care. A research agenda with four priority research questions was defined following identification of the core domains. Precision EM includes consideration of the health care delivery system as a mechanism for improving access to emergency care using data-driven strategies. This provides a unique opportunity to use data and technology to advance systems of care while also centering patients, communities, and equity in these advances.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Investigating Co‐Authorship Networks of Academic and Industry Researchers in Artificial Intelligence

ABSTRACTResearch teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co‐authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.

Emerging trends in survival analysis: Applications and innovations in clinical and epidemiological research

This study provides an in-depth examination of the critical role that survival analysis plays across various disciplines, with a particular emphasis on its applications in clinical research, public policy, and economics. The research is designed to offer a comprehensive overview of traditional survival analysis methods, highlight emerging trends, explore practical applications, and discuss the challenges and future directions for the field. By conducting a thorough review of existing literature and analyzing contemporary advancements, this study employs a robust and methodical approach. Key findings suggest that traditional survival analysis techniques, such as the Kaplan-Meier estimator and the Cox (1072) proportional hazards model, continue to serve as foundational tools in the field. However, the integration of big data and advanced computational technologies has significantly enhanced the precision and broadened the applicability of survival analysis, facilitating more accurate predictions and wider applications. In clinical research, survival analysis remains indispensable for assessing patient outcomes and guiding treatment decisions. Furthermore, the study highlights the growing importance of cross-disciplinary collaborations, which are increasingly essential for addressing both ethical and methodological challenges, thereby enhancing the utility of survival analysis across various sectors. The study concludes that while traditional methods retain their relevance, the future of survival analysis will be shaped by the integration of modern computational tools and the promotion of cross-disciplinary collaborations. Recommendations include further exploration of machine learning and artificial intelligence within survival analysis, as well as encouraging collaborative efforts across diverse fields to address current challenges and expand the application of survival analysis in new and innovative ways.

Research on E-commerce Supply Chain Management HCI in International Trade Using Big Data and CAD Technology

Research on E-commerce Supply Chain Management HCI in International Trade Using Big Data and CAD Technology

Research on the use of communication big data and AI artificial intelligence technology to construct telecom fraud prevention behavior portrait

A solid foundation for behavior portrait construction in the fight against telecom fraud is the goal of this research. The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence. By using insights obtained from a telecom fraud detection model that relies on users’ behavior variations expressed through time-varying signatures, the goal of this study is to enhance fraud prevention strategies in the telecom industry. Through the examination of call detail records and customer profile information, the TeleGuard AI Fraud Prevention Framework (TGAI-FPF) aims to recognize suspicious trends and variations that are potentially suggestive of fraudulent actions. The purpose of the model is to generate behavior portraits that are capable of capturing the distinctive aspects of fraudulent conduct in telecom networks. This will be accomplished through the utilization of advanced analytics and machine learning algorithms. The study highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry. The results of this study should fortify the defenses of telecom networks against growing fraudulent schemes and help in the development of preventative measures to combat fraud. This is the anticipated manner in which the results will add.

THE ROLE OF BIG DATA ANALYSIS IN FASHION DESIGN

Today, as the contemporary, economic and cultural landscape becomes increasingly volatile, the dynamics of the fashion industry are becoming less predictable. The fast-changing fashion industry is constantly generating data. Big data is a powerful tool that offers new and exciting opportunities for fashion designers. It is used for different purposes such as understanding consumer behaviour, predicting trends and creating more personalized products. Design processes that were previously driven by intuition and instinct have become more fluid, data and goal-oriented with the opportunities offered by big data. This article aims to reveal the purposes of using big data by investigating fashion design models based on big data in the fashion industry in recent years. For this purpose, the current models of big data in fashion design, its limitations and development direction are discussed. While many of the areas of use of big data in the fashion industry (trend analysis, demographic, geographical analysis, age and gender, etc.) have been intensively researched, the number of purely design-oriented research is only recently increasing. This study is unique in that it focuses on the design phenomenon and applications of big data in fashion. In this context, there is no academic study in the national literature. It is expected to contribute to the discussion on the effects of big data and big data-based digital technologies in fashion design.

Harnessing Educational Big Data Analytics for Decision-Making in Enhancing School Teaching Quality

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality.

Analysis of the Impact of Digital Transformation on Non-Financial Performance of Manufacturing Enterprises

As one of the important pillars of China’s national economy, the development of the manufacturing industry has attracted much attention. The manufacturing industry has become a key development area due to its high technology content, capital-intensive, knowledge-intensive, and many more characteristics. With the rapid development of big data information technology, emerging technologies such as artificial intelligence have brought a lot of convenience to people’s lives, and manufacturing enterprises have also realized the importance of reform and innovation, and have begun to carry out digital transformation. Based on the study of the motivation of digital transformation of manufacturing enterprises, this paper explores the transformation path of manufacturing enterprises to meet the needs of social development, and deeply analyzes the impact of digital transformation of manufacturing enterprises on enterprise performance, with a view to improving the unbalanced industrial structure and lagging technological development at the present stage through digital transformation.

Student Performance Analysis: A Systematic Research

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.

Empowering nurses to champion Health equity & BE FAIR: Bias elimination for fair and responsible AI in healthcare.

The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively. We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative. Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework. The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies. As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.

Research on the Establishment of Poverty Identification System for College Students under the Background of Big Data

The work of college student financial aid is an important part of talent cultivation in higher education. Combining big data with the identification of impoverished college students and constructing an evaluation system for the identification of impoverished college students based on big data can accurately identify impoverished college students. Based on the concept of financial aid under the background of big data, this paper analyzes the feasibility and necessity of constructing an evaluation system for the identification of impoverished college students, constructs an index system for the identification of impoverished students based on big data, builds a dynamic information support system with the help of big data technology, and constructs the guarantee conditions for accurate identification of impoverished students. It gives full play to the synergy between financial aid and big data information technology, and continuously improves the effectiveness of financial aid in colleges and universities.

A hydrophobic graft-modified PVA hydrogel by michael addition joint with gelation strategy for underwater cardiac sensing

The rapid development of global big health and AI technology has made electrocardiogram (ECG) monitoring in all scenarios more important. While real-time ECG monitoring in the air is easy to realize, underwater ECG monitoring is still challenging for general hydrogels. Herein, we prepared a hydrogel with underwater viscosity, biocompatibility, and excellent mechanical properties by hydrophobic graft-modified of PVA (POV) hydrogel and used it to prepare electrodes for underwater ECG monitoring. The POV hydrogel is very flexible and co-deforms easily with the skin, with a Young's modulus as low as 0.03 MPa and underwater adhesion strength up to 11 KPa. The hydrophobic chain segments within the POV hydrogel give the hydrophobic gel excellent stability, adhesion, and ionic conductivity given by the free ionic groups within the POV hydrogel enable the POV hydrogel to detect and transmit physiological electrical signals. Compared to commercial gel electrodes, this POV electrode has better adhesion, conductivity and stability underwater. The POV gel electrodes can effectively collect real-time ECG signals in the air and underwater. Therefore, this study effectively broaden the application of POV hydrogels in ECG biosensors for underwater cardiac sensing.