Big AI Research Articles

The Convergence of Curating and Technology: A Multidimensional Perspective on Practice

Curation has traditionally been a static practice confined to physical spaces, focusing primarily on the display and interpretation of artworks. However, with the rapid development of digital technologies such as VR, AR, and AI, curatorial practices have evolved, integrating virtual and interactive elements that expand beyond physical locations. This study investigates the impact of digital technologies on contemporary curatorial practices, exploring how tools like big data, VR, and AI are transforming the way curators create interactive, immersive, and multi-layered exhibition experiences. Through case studies and analysis of curatorial projects utilizing these technologies, the research examines the ethical, political, and aesthetic dimensions of curatorial practices and explores the role of technology in enhancing curatorial precision, expanding spatial boundaries, and enabling new forms of audience engagement. The study finds that technologies such as VR and AI provide curators with innovative tools to create dynamic and participatory exhibitions. However, it also highlights the ethical risks associated with overusing technology, including the potential commodification of exhibits and the diminished focus on physical artworks. Digital technologies offer significant potential for expanding and enriching curatorial practices, transforming curation into a bridge between art, culture, and society. Nevertheless, these innovations come with challenges, requiring curators to balance technological integration with maintaining the integrity of traditional curatorial values across ethical, political, and aesthetic dimensions.

Queer ecological data: Where artificial intelligence meets the avian

Nonhuman life is increasingly analyzed and acted upon through big data and AI tools. Birds in particular are among the most datafied wild beings. However, avian—like human—data sets present challenges of bias, misclassification, and harmful collection methods. For example, avian data includes bias along lines of sex and sexuality, female, queer, and intersex birds are significantly understudied. These missing birds not only represent consequences for biodiversity loss but also “naturalize” assumptions about sex and sexuality for all species, including humans. In this article, we interrogate avian datafication practices and introduce Salvaging Birds, a multimodal project proposing “queer data surrogacy” as a method for generating “queer ecological data,” that is data resisting normative environmental frameworks. Here queer data surrogates were produced by creatively subverting AI toward generating speculative missing birds. Bridging critical data and archival studies with queer ecology, we argue that data logics demand examination at nonhuman sites and scales.

UTILIZING AI MODELS TO IDENTIFY AND PREDICT MOOD TRANSITION PATTERNS IN PATIENTS WITH BIPOLAR DISORDER

Abstract Bipolar disorder is characterized by recurrent episodes of depression and mania, making diagnosis and treatment challenging, often leading to poor prognosis. Predicting future episodes based on the assessment of current and past emotional phases in patients can offer valuable insights for clinicians, aiding in the prevention of misdiagnosis and relapse, and in intervention strategies. This study included 812 patients with bipolar disorder from 9 centers, who were assessed online or offline using BDCC, YMRS, and HAMD tools. We employed four AI methodologies to learn from the assessment results and time series, ultimately predicting future emotional phases of the patients. By learning from current assessment results to predict the next emotional phase, the AUCs of the four models were 0.81, 0.70, 0.84, and 0.81, respectively. External validation on 30-day and 90-day emotional phase follow-ups of patients showed prediction accuracies of 0.9, 0.65, 0.86, and 0.62 for each model. While predictive efficacy decreased over time, the average AUC remained above 0.75 at 180 days. Our findings suggest that utilizing big data and AI methods can effectively learn the characteristics of emotional phase transitions in bipolar patients, achieving high accuracy. This model holds significant value for clinical diagnosis and medication guidance for clinicians

Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents: 2024-25 High School Big Data and AI Challenge

The STEM Fellowship High School Big Data and AI Challenge provides students with a unique opportunity to utilize Open Data to investigate one of the UN Sustainable Development Goals while learning data science fundamentals in an experiential learning format – an essential skill set for a young researcher in the digital age. This year students tackled the challenge of Harnessing AI and Open Data Analytics to Combat Social Inequities Among Adolescents. Students suggested their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Optimizing Educational Equity to Advancing Equity for Disabled Youth. These future leaders were tasked with using Open Data to enhance our understanding of social inequities and explore areas for innovation to close inequity gaps and propagate the social pursuit of prosperity for all. Various topics were investigated, identifying different forms of socioeconomic determinants which impact inequity among adolescents on a global scale. By applying computational thinking, students explored the interplay between adolescent inequities and external factors, ultimately contributing to the development of new educational and social development approaches. STEM Fellowship has designed an interdisciplinary, agile educational environment with in-depth learning modules for students as a means to bridge the gap between traditional high school courseware and computational inquiry. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used throughout the program. Additionally, all participants prepared a short slideshow and presented their research to a group of their peers. We are privileged to witness the analytical capabilities of this talented generation of students, and we are confident that they will demonstrate excellence throughout their academic and professional careers. The Western Canada and Eastern Canada finalist events were the culmination of the top participants’ trailblazing research, and were held at the Hunter Hub for Entrepreneurial Thinking at the University of Calgary in Calgary and at Microsoft Canadian Headquarters in Toronto respectively. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge and wish them the best for all of their future endeavours. We also want to express our appreciation to all of the STEM Fellowship volunteers who made this challenge possible. We greatly appreciate the patronage of the program by the Canadian Commission for UNESCO, as well as the Lieutenant Governor of Alberta and the Lieutenant Governor of Ontario. We want to thank Canadian Science Publishing, Environment and Climate Change Canada, Let’s Talk Science, National Research Council Canada, RBC Future Launch, SciNet at the University of Toronto, Hunter Hub at the University of Calgary, Microsoft Canada, Canadian Science Publishing, Overleaf, and Cisco Academy for their invaluable support.

Big data analytics and AI as success factors for online video streaming platforms.

As the trend in the current generation with the use of mobile devices is rapidly increasing, online video streaming has risen to the top in the entertainment industry. These platforms have experienced radical expansion due to the incorporation of Big Data Analytics and Artificial Intelligence which are critical in improving the user interface, improving its functioning, and customization of recommended content. This paper seeks to examine how Big Data Analytics makes it possible to obtain large amounts of data about users and how they view, what they like, or how they behave. While customers benefit from this data by receiving more suitable material, getting better recommendations, and allowing for more efficient content delivery, AI utilizes it. As a result, the study also points to the importance and relevance of such technologies to promote business development, and user interaction and maintain competitiveness in the online video streaming market with examples of their effective application. This work presents a comprehensive investigation of the combined role of Big Data and AI and presents the necessary findings to determine their efficacy as success factors of existing and future video streaming services.

The Role of Azure Data Lake in Scalable and High-Performance Supply Chain Analytics

Growing complexity in global supply chains demands high- performance, scalable data management capabilities. Azure Data Lake offers a cloud-based platform wherein organizations can store, process, and analyze vast volumes of structured and unstructured data in a cost-effective manner. This study discusses how Azure Data Lake elevates supply chain analytics through the convergence of big data technologies, AI, and ML models. It is a mixed-method study, with case studies and statistical analysis being used for performance improvement measurement in supply chain activities. The main findings indicate that companies utilizing Azure Data Lake experience enhanced real-time decision-making, improved demand forecasting, and reduced inefficiencies in operations. However, challenges such as data protection and system integration persist. The paper concludes with sugges- tions for further research and strategies for optimizing Azure Data Lake utilization in supply chain analytics.

Advanced cloud-based real-time credit scoring models: Leveraging big data and AI

Cloud-based credit scoring systems represent a transformative shift in financial technology, leveraging artificial intelligence and machine learning to revolutionize creditworthiness assessment. These platforms integrate traditional and alternative data sources, enabling real-time processing and comprehensive risk evaluation. The evolution from conventional methods has enhanced accessibility for underserved populations while maintaining robust security measures and regulatory compliance. Through advanced technical architecture incorporating data collection, processing, and analysis layers, these systems deliver faster, more accurate credit decisions. The implementation of sophisticated fraud detection mechanisms, coupled with machine learning algorithms, has significantly reduced false positives and improved risk assessment capabilities. Industry leaders like TransUnion demonstrate the practical success of these innovations through improved operational efficiency and expanded market reach. Despite challenges in data quality management and regulatory compliance, the future of credit scoring continues to advance with blockchain integration, enhanced real-time processing, and innovative fraud detection capabilities.

Disruptive Innovation through Additive Manufacturing in India: A Case Study Approach

Known by most as 3D printing, Additive Manufacturing (AM) has become a revolutionary technology that is changing industries worldwide. India has seen a significant uptick in the adoption and application of additive manufacturing in several sectors in recent years, which has resulted in a radical change in production procedures and product creation. The field of additive manufacturing has grown by a strong 9.34% over the past few years. This trend indicates a rebirth of disruptive innovation that is changing production. Application of additive manufacturing can be integrated with Industry 4.0 technologies: big data, AI, and IoT. This will create a growing need for robust digital asset management frameworks. This paper presents a case study of Jayanthi Tooling, an MSME in Pune. The case study highlights the importance of AM adoption among MSMEs, its advantages and challenges, and its impact on the manufacturing landscape in India.

Exploration of Micro-specialty Construction Based on Big Data and AI from the Perspective of Medical and Industrial Integration: A Case Study of Intelligent Technology Aging Application Micro-specialty in Anhui Sanlian University

Exploration of Micro-specialty Construction Based on Big Data and AI from the Perspective of Medical and Industrial Integration: A Case Study of Intelligent Technology Aging Application Micro-specialty in Anhui Sanlian University

High-stable multifunctional dynamically reconfigurable artificial synapses based on hybrid graphene/ferroelectric field-effect transistors

In response to the challenges posed by traditional computing architectures in handling big data and AI demands, neuromorphic computing has emerged as a promising alternative inspired by the brain's efficiency. This study focuses on three-terminal synaptic transistors utilizing graphene and P(VDF-TrFE) to achieve dynamic reconfigurability between excitatory and inhibitory response modes, which are crucial for mimicking biological functions. The devices operate by applying different top gate spikes (±25 V and ±10 V) to modulate the polarization degree of P(VDF-TrFE), thereby regulating the carrier type and concentration in the graphene channel. This results in the effective realization of enhancement and inhibition processes in two neural-like states: excitatory and inhibitory modes, accompanied by good neural plasticity with paired-pulse facilitation and spike-time-dependent plasticity. With these features, the synaptic devices achieve brain-like memory enhancement and human-like perception functions, exhibiting excellent stability, durability over 1000 cycles, and a long retention period exceeding 10 years. Additionally, the performance of the artificial neural network is evaluated for handwritten digit recognition, achieving a high recognition accuracy of 92.28%. Our study showcases the development of highly stable, dynamically reconfigurable artificial synaptic transistors capable of emulating complex neural functions, providing a foundation for emerging neuromorphic computing systems and AI technologies.

Collaborative Research on the Construction of Teaching and Employment Internship Bases for Accounting Major Courses

The rapid development of intelligent technologies such as big data, cloud computing, and AI modeling poses new challenges to the training of accounting professionals. To make up for the defects of the traditional teaching evaluation system that neglects the cultivation of practical ability and improves students' adaptability to employment, this paper constructs an evaluation index system for the teaching of accounting courses and internship bases according to the idea of system theory and the use of the coupled coordination degree model, and based on the principles of scientificity, significance, subjectivity, and independence. It is concluded that: (1) the evaluation of the teaching ability of the accounting profession in colleges and universities needs to comprehensively examine the two dimensions of the teaching of accounting professional courses and the construction of employment internship bases, to realize the organic combination of theoretical and practical education; (2) the synergistic development of the accounting professional courses and employment internship bases is an important part of the evaluation of the teaching ability of the accounting profession in colleges and universities; (3) it is difficult to keep up with the pace of the times for the lack of practical feedback of the theoretical innovations. At the pace of the times, colleges and universities must incorporate the construction of internship bases into the rating system of the teaching ability of accounting and finance to realize the two-way interaction between theory and practice and to grasp the opportunities of the development of digital intelligence technology.

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.

The Impacts of Big Data Analytics and Artificial Intelligence on Marketing Strategies

The marketing sector has seen a significant transformation, particularly due to the emergence of data-driven decision-making and the dominance of digital platforms. This transition signifies a deviation from traditional marketing strategies, which formerly depended on more direct contact methods and conventional market research techniques. As digital technologies have grown, they have changed how we can track and change customers' buying habits and given us new ways to connect with them. Digital platforms and enhanced data provide marketers new consumer insights, making marketing more challenging. A detailed literature review and practical assessment analyse the real and prospective benefits of big data analytics and artificial intelligence on marketing decision-making. According to the paper, AI and big data analytics may assist companies understand customer and industry developments. They might then modify their marketing for each user. Big data analytics and AI improve target market positioning, simplify marketing, and educate consumers, affecting marketing strategy. The research suggests creating a data analysis team, streamlining data gathering and combination, using adaptable analytical tools, and customising marketing efforts. Some issues remain with this research. Data reliability and group size matter. The steps are unclear. For more solid and unambiguous results, future study should examine the impact of marketing using AI and big data analytics, maybe on a particular sector. This research may assist firms greatly enhance their marketing.

REGULATORY AND SUPERVISORY TECHNOLOGY RESEARCH: A BIBLIOMETRIC ANALYSIS

This bibliometric study examines articles on Supervisory Technology (SupTech) and Regulatory Technology (RegTech). The investigation’s main focus was on author trends and keywords. The data was carefully examined from the sources indexed on Dimension.ai, where ‘SupTech’ and ‘RegTech’ were the search terms. A bibliometric map was derived using VOSviewer for bibliometric analysis and a descriptive statistical method. VOSviewer software was used to analyse the development trends of publications related to RegTech and Supertech. In recent years, there has been a sharp increase in the number of papers published on RegTech and SupTech. Various journals have tackled the subject, two of the best being “Fintech and RegTech: Impact on Regulators and Banks” by Ioannis Anagnostopoulos and “The Role of Big Data, Machine Learning, and AI in Assessing Risks: A Regulatory Perspective” by Scott W. Bauguess. The most frequently used terms include regulation, supervision, compliance, finance, and technology. Furthermore, over the past ten years, Douglas W. Arner and Simone Di Castri have authored the most articles of any authors. Challenge and Study are two of this theme’s most commonly cited keywords. Digitalisation is critical today and presents a challenge for practitioners and researchers. Academics working in RegTech and SupTech can find relevant information in this study, which gives an overview of keyword trends and the most popular scholars on the subject.

The Impact of Financial Technology on Green Credit: An Empirical Analysis Based on Commercial Banks

This study explores how financial technology influences green credit operations in China's commercial banks, using data from over 30 listed banks (20112021) and a financial technology development index built with text mining. Financial technology enhances green credit by optimizing resource allocation, directing funds to environmentally compliant projects, and improving risk assessment with big data and AI, thereby lowering bad loan ratios. It also increases efficiency through automation and faster approvals, shortening credit cycles. Bank operating conditions significantly impact these benefits, highlighting financial technology's role in mitigating risks and driving China's green economic transformation.

DATA 5.0-Data Acquisition, Translation & Analysis-a prospective urooncological data warehouse for the 21st century.

Prospective data registration is the basis of clinical oncological research. Commonly, case documentation is restricted to studies investigating a defined hypothesis. Only few institutions prospectively register all oncological patients with a reliable, sustainable and continuous follow-up infrastructure. The Department of Urology of the Heidelberg University Hospital started its prospective tumor data base in 1992. Since then, the clinical course of all oncological in-patients is continuously registered within a life-long follow-up (success rate: 93%). Associated tumor tissue is stored in the Heidelberg Biobank. In 2005, the transfer of this invaluable registry from the initial InterSystemsCache®/KRAZTUR system to a modern data warehouse was initiated. However, the transfer of existing data into a new environment proved to be technically challenging. To migrate the existing data into a modern data warehouse (DATA 5.0) while maintaining data extraction functions. Additional requirements included FHIR connectivity, big data analyses and AI applications. Together with SAP SE, DATA 5.0 was developed. Based on SAP HANA® (High Performance Analytic Appliance) it allows data registration and analysis with third party analytical tools. The project was supported by members of the SAP SE executive board and funded by the Dietmar Hopp Foundation. Data Acquisition, Translation & Analysis 5.0 (DATA 5.0), a web-based tool for data registration, preservation and analysis of treatment and follow-up data, was developed to proof-of-concept stage. DATA 5.0 was then implemented into clinical practice replacing the previous system. As of today, 15,345 oncological patients and 6.7 Mio. data points are registered. Prospective long-term data was successfully migrated into DATA 5.0, allowing data preservation, flexibility and capabilities for future data sources. DATA 5.0, together with associated tumor tissue, is a lighthouse platform for oncological research, with capability for third party analytical tools, big data analysis and AI applications including training of digital twin models.

Analysis of online agent accounting platform under big data AI environment

Analysis of online agent accounting platform under big data AI environment

Exploring the Intersection of Big Data and AI With CRM Through Descriptive, Network, and Contextual Methods

Exploring the Intersection of Big Data and AI With CRM Through Descriptive, Network, and Contextual Methods

Financial Optimization in the Automotive Industry: Leveraging Cloud-Driven Big Data and AI for Cost Reduction and Revenue Growth

Financial Optimization in the Automotive Industry: Leveraging Cloud-Driven Big Data and AI for Cost Reduction and Revenue Growth

Advanced Encryption Techniques in Biometric Payment Systems: A Big Data and AI Perspective

Advanced Encryption Techniques in Biometric Payment Systems: A Big Data and AI Perspective