Big AI Research Articles

Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases

With the rise of social media and the internet, the rapid dissemination of information has increased the likelihood of reputation infringement. This study utilizes judicial big data and AI to analyze intrinsic connections in reputation infringement cases, aiding judges in delivering consistent rulings. The challenge lies in balancing freedom of speech with the right to reputation and addressing the ambiguity and subjectivity in infringement cases. This research constructs a structured reputation infringement case dataset from Chinese Judgments Online. It introduces a Fractional Fuzzy Neural System (FFNS) to tackle the vagueness in reputation infringement acts and judicial language, enhancing prediction accuracy for case outcomes. The FFNS, integrating fractional calculus, fuzzy logic, and neural networks, excels in adaptability and nonlinear modeling. It uses fractional order fuzzy membership functions to depict the extent and severity of reputation infringement accurately, combining these outputs with neural networks for predictive analysis. The result is a more precise adjudication tool, demonstrating significant potential for judicial application.

Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence

Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.

4IR 시대의 스마트물류 플랫폼 활용역량이 데이터 기반 수요 예측 성과에 미치는 영향에 관한 실증연구

The rapid emergence of Industry 4.0 technologies, such as big data, IoT, and AI, has significantly transformed the distribution and logistics landscape. Smart logistics platforms are increasingly adopted in this sector, leading to improvements in various aspects, including demand forecasting accuracy. This study aims to investigate the mediating effects of data compatibility, real-time risk response, and product history tracking on the relationship between a company’s smart logistics platform utilization capability and its data-driven demand forecasting performance. Drawing upon theoretical insights from existing literature, this study develops a research model and corresponding hypotheses. Operationalization of key constructs is achieved through the development of a structured questionnaire based on established measurement scales. To ensure the representativeness of our sample, the survey is distributed to professionals utilizing smart logistics platforms in domestic distribution and logistics companies. Following a rigorous data collection process, a total of 280 valid responses are retained for further analysis. This study aims to highlight the growing importance of smart logistics platform utilization capability as smart technologies advance. Based on the research results, it presents practical situations in the industrial field and provides policy implications for supporting small and medium-sized enterprises (SMEs) to government agencies and local governments.

빅데이터의 기능과 역할 논의

The rise of the fourth industrial revolution has catalyzed the rapid development of information and communications technologies like AI, big data, and IoT, driving changes in international politics and diplomacy. This study examines the significance of big data and its application as a diplomatic tool, scrutinizing domestic and international instances.The analysis found that using big data and AI technologies in risk management and international politics is emerging as crucial elements of globalization and national influence. Nevertheless, the process of incorporating big data into diplomacy necessitates the resolution of current obstacles. Various applications to expedite diplomatic activities will become more viable when related protocols are defined and concerns about the ethics of managing big data are addressed.

Key factors driving the potential future developments in automated manufacturing at Hindustan aeronautics ltd (HAL)

This research aims to explore the key factors driving the application of Big Data and IoT/AI in potential future developments of automated manufacturing at Hindustan Aeronautics Ltd (HAL). By investigating the current state of automated manufacturing, the study will identify the benefits and challenges associated with integrating Big Data, IoT, and AI technologies. The research questions will focus on understanding the factors influencing the adoption of these technologies, their impact on manufacturing processes, and the potential benefits they offer to HAL. The research objectives include analysing the current landscape of automated manufacturing, identifying key factors driving the application of Big Data and IoT/AI, assessing the benefits and challenges of implementing these technologies, and providing recommendations for HAL's future developments in automated manufacturing.

Big data in the business environment: an analysis of its contributions to Competitiveness. A Literature Review

In the era of Industry 4.0, characterized by transformative technological advancements reshaping manufacturing processes, big data has become a common practice in business intelligence. It encompasses the use of data with advanced analytics techniques and plays an important role in business aspects and customer choice. In this context, the primary goal of this research is to comprehend the relationship between big data and the competitiveness of businesses. The research is based on a review of 83 articles published on the Web of Science in the period 2016 and 2023. Through cluster analysis, four groups of research categories are identified in this area (big data and AI in Industry 4.0, analysis of data for decision-making, big data and business innovation, and Internet of Things as a data source). The practical implications of this research are pertinent to organizational management activities involving innovation processes and decision-making, with direct implications for small and midsize enterprises competitiveness. On a theoretical level, the identified categories provide a framework for future research in understanding the connection between big data and competitiveness in the context of industry 4.0.

Study on the coordinated development of national traditional sports and tourism brands based on big data platforms from the perspective of “The Belt and Road”

The Belt and Road (B&R) plan is put out within the framework of global economics and strategic growth. This study examines the written material of popular tourist sites along B&R and the tourism assets from the viewpoint of B&R, based on the wireless network and AI technology, and using a big data platform and the Internet of Things (IoT) User Generated Content (UGC) network structure. To manage tourist pictures from customers’ views, online travel notes are first utilized as examples. Next, tourism texts’ keywords are extracted using Python big data and AI technology to understand consumers’ perceptions of scenic spot preferences, tourism facilities and services, and social and cultural customs. The findings demonstrate that, when compared to the conventional tourism brand development strategy, the integrated development strategy based on the AI big data platform can not only increase the effectiveness of managing tourists’ perceptions of scenic locations but can also encourage the common development of national sports event components and intelligent tourism image management. Several sports tourist boutique picturesque locations have also been built along B&R following years of development of intelligent tourism and sports projects, which will strengthen the effect of multicultural exchanges.

Big Data, Artificial Intelligence, and Financial Literacy: Exploring their Combined Influence on Investment Behavior among Chinese Household

The investing behavior of Chinese families is undergoing a dramatic transition in the context of the digital financial era, impacted by factors such as big data use, AI adoption, financial literacy, digital literacy, and risk aversion. Although prior research has offered useful insights into certain components, a thorough examination of their linked dynamics has been lacking. The purpose of this research was to look into how big data usage, AI adoption, financial literacy, digital literacy, and risk aversion influence investment behavior among Chinese households. Additionally, it aimed to learn more about how risk aversion and digital literacy function as mediators in these relationships. A questionnaire-based survey of 370 Chinese families was employed as part of the quantitative research methodology. The study employed AMOS to find the relationship between variables. The research found that big data usage, AI adoption, financial literacy, and digital literacy significantly and favorably influenced Chinese households' investment behavior. It was discovered that digital literacy mediated the linkages between the adoption of technology and investment decisions. Furthermore, risk aversion reduced the effects of financial literacy and big data usage on investment behavior. This study added to the body of knowledge by providing a comprehensive framework that incorporates several aspects impacting investment behavior. It shed insight into the complicated dynamics of technology uptake and literacy, as well as their impact on investment decisions. The study went beyond individual components to investigate their interactions, resulting in a more complex view of modern investment behavior. This study has broad-ranging effects that will help investors, financial institutions, governments, educators, and researchers. The focus on a particular setting and self-reported data are two important constraints that must be acknowledged. Future studies can investigate longitudinal dynamics and cross-cultural variances to further our understanding of investment behavior in the digital age.

Unlocking the potential of big data and AI in medicine: insights from biobanking.

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

The Paradigm Shift in Education in Big Data Era: Exploring the Intersection of Historical Assessment Frameworks and AI-Powered Assessment Methods in Education

This paper delves into the dynamic intersection between traditional assessment processes and AI-powered technology assessment models within the expansive landscape of the big data era. Tracing the origins of large-scale standardized testing, from Francis Galton's pioneering intelligence testing proposals to the subsequent development of norm-referenced testing (NRT) by Alfred Binet and Theodore Simon, the historical trajectory underscores the evolution towards authentic assessment paradigms such as computerized adaptive testing (CAT) and performance-based assessment (PBA). These shifts reflect a broader aim of equipping educators with tools to measure student learning effectively and foster continuous learning beyond the confines of the classroom. However, despite the proliferation of methods integrating big data and AI technologies in educational settings, a discernible gap persists between technological capabilities and their practical implementation in education, notably in the realm of student assessment and evaluation. This gap highlights challenges in enhancing assessment accuracy and reliability, evaluating non-cognitive skills, and delivering personalized feedback. The introduction of AI-powered technologies like ChatGPT raises ethical considerations regarding fairness, privacy, and bias, necessitating responsible and equitable deployment.This paper addresses fundamental research questions concerning the historical influences on assessment frameworks, the role of AI-powered technology in driving paradigm shifts, and the ethical considerations surrounding its application. Through a comprehensive literature review, it explores the potential of AI technology to enhance assessment accuracy and reliability while advocating for its responsible use. Despite the challenges posed by traditional assessment processes and AI-powered technology assessment models, educators continually strive to improve student learning and readiness for lifelong learning.By celebrating the invaluable contributions of educators, we can shift the narrative surrounding assessment practices towards a more positive and supportive environment, fostering ethical and innovative uses of AI-powered technology. This celebration serves as inspiration for future research and development in assessment, emphasizing the importance of building upon educators’ innovative practices to shape the future of assessment in the big data era.

Assessing the Complex Interplay of China’s Fertility Policy Adjustments and Female Employment Dynamics: An In-depth Analysis of the Digitalized HRM Landscape in the Age of AI and Big Data

The fertility policy adjustments are occurring against a backdrop of rapid technological advancement, characterized by the integration of big data analytics and artificial intelligence (AI) into human resource management (HRM) practices. In the banking sector, as in many other industries, the adoption of these technologies has become increasingly pervasive. This study explores the intricate relationship between fertility policy adjustments, the integration of big data and AI in HRM practices, and employee satisfaction within China's banking sector. In response to evolving demographic and technological landscapes, the research aims to uncover how fertility policy adjustments influence female employment dynamics, the adoption of big data and AI in HRM, and ultimately, employee satisfaction. Utilizing a quantitative research design, structured surveys were administered to female bank employees. The resulting data were rigorously analyzed using the Statistical Package for the Social Sciences (SPSS). The study underscores the practical significance of optimizing HR technologies, particularly big data analytics and AI, for enhancing both HR functions and employee satisfaction. It also emphasizes the importance of data-driven HR practices and predictive employee retention strategies as crucial tools in creating responsive and supportive work environments. Additionally, this research contributes to HRM theory by recognizing the pivotal role that technology integration plays in shaping modern HR strategies and organizational success. While acknowledging its limitations, this study lays the foundation for future research, including studies that are longitudinal, comparative, and qualitative studies, to offer a more comprehensive understanding of the complex dynamics in the contemporary workplace.

Perspectives of patients and clinicians on big data and AI in health: a comparative empirical investigation

Abstract Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we conducted semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings.

A Comprehensive Study on Integration of Big Data and AI in Financial Industry and its Effect on Present and Future Opportunities

This study evaluates the substantial influence of AI-technologies in the finance industry, with advancement expected to accelerate in the next few years. It also forecasts the expansion of AI adoption across various business sectors and the integration of AI-based operational networks with existing commercial systems to meet consumer demands. The financial industry will blend AI-based transaction channels with established systems, enhancing the customer experience and efficiency. This integration will streamline and advance transaction processes, making them more responsive to customer demands. The standard change in customer dealings is expected to be a significant transformation in the financial sector. The transformative potential of Big Data and AI in the financial sector goes beyond operational improvements; these technologies will create new opportunities for growth and development, giving financial institutions a modest point in operational efficiency and innovative product and service offerings. This study aims to investigate the overall impact of the convergence of Big Data and AI on the financial industry. It anticipates increased revolution, diversification of commercial applications, and smooth AI integration into existing systems. These technologies will shape the financial environment in the future, offering new opportunities for industry participants and consumers.

The impact of AI-based cyber security on the banking and financial sectors

BD and AI are now transforming the banking and finance industry at a very fast pace, which is leading to change in the banking and finance institutions. This change is making them better, customer-oriented and financially rewarding organizations. Big data and AI have been useful in the banking and financial institutions to assess and manage the risks. Through the analysis of big amounts of unstructured data in real time, AI algorithms are capable of identifying risks. This makes it easy to put preventive measures in place to avert the risks. In addition, big data and AI have come a long way in solving the problem of fraud in banking and finance. This paper showed how big data and AI improve risk management, Cyber threat, and fraud in banking and finance by using data analysis and data pattern identification in real-time. That is why our work emphasizes the importance of implementing secure privacy and explaining the AI algorithm to eliminate ethical and Cyber security issues. Using analytical approaches, AI can identify the transactions with the help of comparison with the previous data and the behavioral characteristics related to the fraud. This approach to fraud prevention has been effective in reducing losses while at the same time improving the customer’s confidence in the company. On the other hand, there are disadvantages of big data and AI such as privacy, security, and ethical issues. Measures that can be used to safeguard customer information have to be employed in order to effectively safeguard the consumer data. Furthermore, transparency and accountability of the AI algorithms are crucial in order to avoid unfair decisions.

Big data and AI empower innovative practices in low-carbon production processes

Big data and AI empower innovative practices in low-carbon production processes

Healthcare Analytics: Conceptualizing a Research Agenda

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further.

The Application of Big Data and AI in Risk Control Models: Safeguarding User Security

The Application of Big Data and AI in Risk Control Models: Safeguarding User Security

Application analysis of big data and AI technology in new media communication channels

Application analysis of big data and AI technology in new media communication channels

IoT-Driven Smart Cities: Leveraging Big Data and AI for Sustainable Urban Development

IoT-Driven Smart Cities: Leveraging Big Data and AI for Sustainable Urban Development

Leveraging market insights for sustainable agricultural practices

The integration of market insights into agricultural strategy is increasingly recognized as a pivotal element for enhancing the sustainability of agricultural practices. This article examines how market-driven data can inform and refine sustainable farming techniques within the agro-industrial complex. By analyzing consumer trends, pricing patterns, and demand fluctuations, agricultural stakeholders can align their practices more closely with environmental sustainability goals and market demands. We explore several case studies where market insights have directly influenced agricultural decision-making, leading to more efficient use of resources and reduced environmental impact. These include the adoption of organic farming practices in response to rising consumer demand for organic produce, and the strategic placement of crops based on predictive analytics of market trends. Furthermore, the article discusses the role of digital technologies, such as big data analytics and AI, in capturing and analyzing market data to facilitate informed decision-making. Key findings suggest that leveraging market insights not only enhances the economic viability of agricultural enterprises but also promotes environmental stewardship and resource sustainability. The conclusion highlights the need for a collaborative approach between market analysts, agronomists, and farmers to foster a sustainable, market-responsive agricultural framework.