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Journal articles on the topic 'Artificial Intelligence and BIG Data'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Fink, Leonard. "Big Data and Artificial Intelligence." Zeitschrift für geistiges Eigentum 9, no. 3 (2017): 288. http://dx.doi.org/10.1628/186723717x15069451170874.

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Nayak, BK. "Big data and artificial intelligence." Global Journal of Cataract Surgery and Research in Ophthalmology 2 (June 9, 2023): 1–2. http://dx.doi.org/10.25259/gjcsro_12_2023.

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Langner, Soenke, Ebba Beller, and Felix Streckenbach. "Artificial Intelligence and Big Data." Klinische Monatsblätter für Augenheilkunde 237, no. 12 (2020): 1438–41. http://dx.doi.org/10.1055/a-1303-6482.

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AbstractMedical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

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O'Leary, Daniel E. "Artificial Intelligence and Big Data." IEEE Intelligent Systems 28, no. 2 (2013): 96–99. http://dx.doi.org/10.1109/mis.2013.39.

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Klipa, Djuro, Igor Ristić, Aleksandar Radonjić, and Ivan Scepanović. "BIG DATA AND ARTIFICIAL INTELLIGENCE." International Journal of Management Trends: Key Concepts and Research 1, no. 1 (2022): 3–14. http://dx.doi.org/10.58898/ijmt.v1i1.03-14.

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The Big Data embodies a technology that permits the storage, processing, and management of broad and complex data sets in which traditional data processing applications are not applicable. These sets of data are usually characterized by a substantial volume of information that they carry, variety, versatility in terms of the format in which they are written, as well as a high-speed ingress which is often greater than the speed of processing. A particular challenge is the data which are coming from the Internet of Things (IoT) “world” that is constantly expanding and which already consists of several billion devices that can measure various parameters in the environment, communicate, process and transmit information. The data streams emanating from these devices are changing traditional approaches to data management and contribute to the emergence of the Big Data paradigm. This paper discusses the characteristics of the IoT infrastructure in terms of vast scale sensor applications and a possibility of connectivity of sensor networks, as well as various techniques of collection, storage, archiving and processing of the data on the cloud.

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Kersting, Kristian, and Ulrich Meyer. "From Big Data to Big Artificial Intelligence?" KI - Künstliche Intelligenz 32, no. 1 (2018): 3–8. http://dx.doi.org/10.1007/s13218-017-0523-7.

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Amaro Junior, Edson. "Artificial intelligence and Big Data in neurology." Arquivos de Neuro-Psiquiatria 80, no. 5 suppl 1 (2022): 342–47. http://dx.doi.org/10.1590/0004-282x-anp-2022-s139.

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ABSTRACT Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, and operational efficiency. Keywords: Big Data, Traditional Data, Data Warehousing, Business Intelligence (BI), Artificial Intelligence (AI), Data Science, NoSQL, Data Ecosystem, Data Architecture, Data Integration, Data Processing, Data Storage, Actionable Insights, Data-Driven Decision Making, Innovation, Scalable Data Solutions, Data-Driven Organizations, Data Synergy, Technology Collaboration, Digital Trans

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Ontaneda, Daniel, Lindsay A. Ross, and Reinhard Hohlfeld. "MRI, Big Data, and Artificial Intelligence." Neurology 97, no. 21 (2021): 975–76. http://dx.doi.org/10.1212/wnl.0000000000012883.

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Rush, Barret, David J. Stone, and Leo Anthony Celi. "From Big Data to Artificial Intelligence." Critical Care Medicine 46, no. 2 (2018): 345–46. http://dx.doi.org/10.1097/ccm.0000000000002892.

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Wang, Sophia Y., Suzann Pershing, and Aaron Y. Lee. "Big data requirements for artificial intelligence." Current Opinion in Ophthalmology 31, no. 5 (2020): 318–23. http://dx.doi.org/10.1097/icu.0000000000000676.

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Kantarjian, Hagop, and Peter Paul Yu. "Artificial Intelligence, Big Data, and Cancer." JAMA Oncology 1, no. 5 (2015): 573. http://dx.doi.org/10.1001/jamaoncol.2015.1203.

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Oliveira, Arlindo L. "Biotechnology, Big Data and Artificial Intelligence." Biotechnology Journal 14, no. 8 (2019): 1800613. http://dx.doi.org/10.1002/biot.201800613.

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Gandomi, Amir H., Fang Chen, and Laith Abualigah. "Big Data Analytics Using Artificial Intelligence." Electronics 12, no. 4 (2023): 957. http://dx.doi.org/10.3390/electronics12040957.

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Schaich Borg, Jana. "Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap." Big Data & Society 8, no. 2 (2021): 205395172110401. http://dx.doi.org/10.1177/20539517211040197.

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Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

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Zou, Feng, Lizhu Ye, Yunxiang Liu, and Yunfeng Zhou. "The Application of Big Data Technology in Computer Artificial Intelligence Technology." Journal of Physics: Conference Series 2023, no. 1 (2021): 012006. http://dx.doi.org/10.1088/1742-6596/2023/1/012006.

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Abstract Compared with the traditional artificial intelligence design, the artificial intelligence automation design of big data has obvious economic applicability. In recent years, with the continuous development of the field of artificial intelligence design in China, artificial intelligence design has been effectively innovated and promoted, and big data technology, as the representative of which, also plays an obvious role. By expatiating the design, manufacturing and construction concepts of artificial intelligence in the new era, this paper analyzes the principle of big data technology and its deep integration with artificial intelligence.

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Qurbonova, Barchinoy, Sevarakhon Sulaymanova, Nazokat Akhmedova, and Muhammadqodir Yunusaliyev. "Big Data, Artificial Intelligence and Smart Cities." E3S Web of Conferences 402 (2023): 03013. http://dx.doi.org/10.1051/e3sconf/202340203013.

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Cities are gradually relying on significant segments to handle problems with civilization, the environment, topography, and many other topics. This possibility is greatly aided by the burgeoning idea of “Green Infrastructure,” which fosters the integration of monitors and Big Data through the Internet of Things (IoT). In addition to improving employment future, this data explosion also opens up new opportunities for city planning and administration. Machine life big data processing may significantly improve the urban fabric, but ecology and livability factors shouldn’t be ignored in favour of technical ones. To comply with the Sustainability Goa and the New Urban Objectives, this paper analyses the urban AI’s potential and suggests a new feel great Artificial intelligence and metropolises while helping to ensure the incorporation of key facets of Culture, Insulin sensitivity, and Leadership. These aspects are known to be critical for the successful integration of Smart Cities. In order to improve the life quality of the urban fabric and foster job creation and opportunity, this document is directed at government officials, computer scientists, and engineering who are interested in strengthening the convergence of artificial intelligence and big data in smart cities.

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Venugopal, Ragukumar. "Artificial intelligence and big data in healthcare." IHOPE Journal of Ophthalmology 2 (May 6, 2023): 49–53. http://dx.doi.org/10.25259/ihopejo_31_2022.

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Du, Yuyao, Zihan Liao, and Jingyi Teng. "Artificial intelligence and big data assist investment." SHS Web of Conferences 218 (2025): 01003. https://doi.org/10.1051/shsconf/202521801003.

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Excessive stock market volatility amplifies financial risks, necessitating AI-driven decision-support systems for risk mitigation. Integrating AI and big data analytics enables real-time market monitoring and actionable insights for investors and regulators, enhancing global financial profitability through improved forecasting and risk management. This study combines game theory principles with AI technologies to develop market stabilization strategies, simulating multi-agent interactions to optimize trading rules and regulatory frameworks. Through price game modeling, it examines price dynamics and investor strategy equilibria across time horizons, highlighting AI’s dual role in predicting volatility and balancing short-term gains with long-term stability. While addressing stakeholder conflicts, the research acknowledges AI’s complex challenges alongside its transformative potential. The findings offer practical guidance for leveraging AI’s benefits in finance while managing risks, contributing to sustainable economic growth through enhanced understanding of intelligent systems’ operational mechanisms.

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Han, Limin. "Research on Big Data and Artificial Intelligence Driven Human Resource Management Innovation." Frontiers in Business, Economics and Management 14, no. 1 (2024): 7–10. http://dx.doi.org/10.54097/c28e5c13.

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This paper focuses on the application and innovation of big data and artificial intelligence in the field of human resource management. With the continuous development of information technology, big data and artificial intelligence have become indispensable and important tools in enterprise management. In the field of human resource management, the application of big data and artificial intelligence provides enterprises with more accurate and efficient management methods and promotes the innovation and development of human resource management. This paper first introduces the basic concepts and characteristics of big data and artificial intelligence, and then analyzes their applications and innovations in recruitment, training, performance management, and employee benefits. Next, the paper discusses specific cases of big data and artificial intelligence in human resource management and evaluates their effects and impacts. Finally, the paper summarizes the roles and challenges of big data and artificial intelligence in human resource management and looks ahead to the future.

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Yuan, Yumin, and Xiyuan Li. "Application Strategies of Artificial Intelligence and Big Data Technology in Computer Monitoring and Control." Journal of Electronic Research and Application 9, no. 2 (2025): 29–34. https://doi.org/10.26689/jera.v9i2.9916.

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This article focuses on the current computer monitoring and control as the research direction, studying the application strategies of artificial intelligence and big data technology in this field. It includes an introduction to artificial intelligence and big data technology, the application strategies of artificial intelligence and big data technology in computer hardware, software, and network monitoring, as well as the application strategies of artificial intelligence and big data technology in computer process, access, and network control. This analysis aims to serve as a reference for the application of artificial intelligence and big data technology in computer monitoring and control, ultimately enhancing the security of computer systems.

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Cremer, Stefan, and Claudia Loebbecke. "Artificial Intelligence Imagery Analysis Fostering Big Data Analytics." Future Internet 11, no. 8 (2019): 178. http://dx.doi.org/10.3390/fi11080178.

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In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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Carlos, Ruth C., Charles E. Kahn, and Safwan Halabi. "Data Science: Big Data, Machine Learning, and Artificial Intelligence." Journal of the American College of Radiology 15, no. 3 (2018): 497–98. http://dx.doi.org/10.1016/j.jacr.2018.01.029.

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24

Sun, Zhaohao. "Big Data 4.0: The Era of Big Intelligence." Journal of Computer Science Research 6, no. 1 (2024): 1–15. http://dx.doi.org/10.30564/jcsr.v6i1.6054.

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Big data has had significant impacts on our lives, economies, academia and industries over the past decade. The current questions are: What is the future of big data? What era do we live in? This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta (big data) to big intelligence. More specifically, this article will analyze big data from an evolutionary perspective. The article overviews data, information, knowledge, and intelligence (DIKI) and reveals their relationships. After analyzing meta as an operation, this article explores Meta (DIKE) and its relationship. It reveals 5 Bigs consisting of big data, big information, big knowledge, big intelligence and big analytics. Applying meta on 5 Bigs, this article infers that Big Data 4.0 = meta4 (big data) = big intelligence. This article analyzes how intelligent big analytics support big intelligence. The proposed approach in this research might facilitate the research and development of big data, big data analytics, business intelligence, artificial intelligence, and data science.

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Chałubińska-Jentkiewicz, Katarzyna, and Monika Nowikowska. "Artificial Intelligence v. Personal Data." Polish Political Science Yearbook 51 (December 31, 2022): 183–91. http://dx.doi.org/10.15804/ppsy202240.

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The world is constantly changing under the influence of new technologies. Artificial intelligence systems are currently used in many areas of human activity. Such systems are increasingly assigned the tasks of collecting and analysing personal data. The areas successfully using AI include transport, medicine, trade, marketing, and others. The number of these areas increases proportionally with the advancement of technology. We can process vast amounts of data and analyse it using IA. It is, of course, big data that sits at the heart of AI. As computing systems generally have grown in power and capacity, data consumption has grown exponentially.

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Dan, Songjian. "Teacher Intelligence Training Based on Big Data and Artificial Intelligence." International Journal of e-Collaboration 18, no. 3 (2022): 1–11. http://dx.doi.org/10.4018/ijec.307137.

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The purpose of this paper is to improve the theoretical system of teacher's intelligent training, build a teacher's intelligent training platform, build an intelligent training course resource and establish a teacher's intelligent training mechanism. This article mainly uses the methods of investigation and interviews to analyze the status quo of teachers' professional development, existing problems, and research needs. The results of the questionnaire survey showed that 46.7% of participants were in favor of smart training, but there were also 70 people who believed that the biggest difficulty in smart training was the lack of a relatively fixed and effective platform. Therefore, it is necessary to design an intelligent training platform to provide a good platform for teachers to learn.

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Xie, Dan, and Yu He. "Marketing Strategy of Rural Tourism Based on Big Data and Artificial Intelligence." Mobile Information Systems 2022 (June 30, 2022): 1–7. http://dx.doi.org/10.1155/2022/9154351.

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In recent years, with the rapid development of artificial intelligence technology and big data application and the continuous optimization of algorithms such as deep learning algorithms and reinforcement learning algorithms, tourist attractions using “smart tourism” technology can obtain more accurate and in-depth knowledge from tourism big data and mine the value behind the data. As an important branch of tourism, the rise of rural tourism has inspired rural areas to protect the local environment, inherit characteristic culture, develop tourism resources, develop tourism economy, and increase rural income, which has well responded to the call of the rural revitalization strategy. However, due to lack of information resources and technology, some rural scenic spots still adopt traditional marketing methods without the support of big data and artificial intelligence technology. This paper proposes a research work on the marketing strategy of rural tourist attractions based on big data and artificial intelligence, analyzes current application of big data and artificial intelligence in rural tourism, and discusses the transformation of rural tourism marketing to a new fusion model with the support of big data and artificial intelligence, aiming to study every step involved and supported deeply by big data and artificial intelligence, from the awakening of intentions before the travel to the analysis of behaviors and preferences during the travel and the collection of evaluation after the travel. The research results of this paper show that more sufficient and deep integration between rural tourism and artificial intelligence/big data are needed to make tourists get better tour experience and marketing strategy of rural scenic spots to get better effects and income.

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Abidi, Syed Sibte Raza, and Samina Raza Abidi. "Intelligent health data analytics: A convergence of artificial intelligence and big data." Healthcare Management Forum 32, no. 4 (2019): 178–82. http://dx.doi.org/10.1177/0840470419846134.

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Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

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Bonami, Beatrice, Luiz Piazentini, and André Dala-Possa. "Education, Big Data and Artificial Intelligence: Mixed methods in digital platforms." Comunicar 28, no. 65 (2020): 43–52. http://dx.doi.org/10.3916/c65-2020-04.

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Digital technology has provided users with new connections that have reset our understanding of social architectures. As a reaction to Artificial Intelligence (AI) and Big Data, the educational field has rearranged its structure to consider human and non-human stakeholders and their actions on digital platforms. In light of this increasingly complex scenario, this proposal aims to present definitions and discussions about AI and Big Data from the academic field or published by international organizations. The study of AI and Big Data goes beyond the search for mere computational power and instead focuses upon less difficult (yet perhaps more complex) areas of the study social impacts in Education. This research suggests an analysis of education through 21st century skills and the impact of AI development in the age of platforms, undergoing three methodological considerations: research, application and evaluation. To accomplish the research, we relied upon systematic reviews, bibliographic research and quality analyses conducted within case studies to compose a position paper that sheds light on how AI and Big Data work and on what level they can be applied in the field of education. Our goal is to offer a triangular analysis under a multimodal approach to better understand the interface between education and new technological prospects, taking into consideration qualitative and quantitative procedures. La tecnología digital ha traído características de conexión que restablecen nuestra comprensión de arquitecturas sociales. Sobre la Inteligencia Artificial (IA) y Big Data, el campo educativo reorganiza su estructura para considerar a los actores humanos y no humanos y sus acciones en plataformas digitales. En este escenario cada vez más complejo, esta propuesta tiene como objetivo presentar definiciones y debates sobre IA y Big Data de naturaleza académica o publicados por organizaciones internacionales. El estudio de IA y Big Data puede ir más allá de la búsqueda de poder computacional / lógico y entrar en áreas menos difíciles (y quizás más complejas) del campo científico para responder a sus impactos sociales en la educación. Esta investigación sugiere un análisis de la educación a través de las habilidades del siglo XXI y los impactos del desarrollo de IA en la era de las plataformas, pasando por tres ejes de grupos metodológicos: investigación, aplicación y evaluación. Para llevar a cabo la investigación, confiamos en revisiones sistemáticas, investigaciones bibliográficas y análisis de calidad de estudios de casos para componer un documento de posición que arroje luz sobre cómo funcionan la IA y el Big Data y en qué nivel se pueden aplicar en el campo de la educación. Nuestro objetivo es ofrecer un análisis triangular bajo un enfoque multimodal para comprender mejor la interfaz entre la educación y las nuevas perspectivas tecnológicas.

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Correia, de Lima Fabiano. "Examining the Inseparable Relationship between Big Data and Artificial Intelligence." Examining The Inseparable Relationship Between Big Data and Artificial Intelligence 8, no. 3 (2024): 3. https://doi.org/10.5281/zenodo.13869007.

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This article explores the synergistic relationship between Big Data and Artificial Intelligence (AI), focusing on how Big Data provides the vast datasets required for AI algorithms to function effectively, while AI enhances Big Data analysis for better decision-making, automation, and innovation. Additionally, the article delves into the challenges, such as data privacy concerns, computational limitations, and the high costs associated with maintaining AI systems, while highlighting the immense potential for these technologies to revolutionize industries like healthcare, finance, and e-commerce. We will explore the future developments of AI processing power, the potential for DNA digital storage, and the shift toward green energy to address the environmental impact of data processing.

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ЕРМОЛАЕВ, К. Н., and К. М. ВОЙТОВИЧ. "THE POSSIBILITY OF USING ARTIFICIAL INTELLIGENCE AND BIG DATA IN INSURANCE." Экономика и предпринимательство, no. 10(159) (December 4, 2023): 1178–80. http://dx.doi.org/10.34925/eip.2023.159.10.240.

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В статье представлен всесторонний обзор важной роли, которую играют искусственный интеллект (ИИ) и большие данные в страховой отрасли. Это подчеркивает преобразующий потенциал искусственного интеллекта, особенно машинного обучения, в автоматизации задач, которые ранее требовали вмешательства человека. Использование искусственного интеллекта в страховании охватывает несколько ключевых областей, таких как эффективное управление бизнесом и повышение качества обслуживания клиентов. The article provides a comprehensive overview of the important role played by artificial intelligence (AI) and big data in the insurance industry. This highlights the transformative potential of artificial intelligence, especially machine learning, in automating tasks that previously required human intervention. The use of artificial intelligence in insurance covers several key areas, such as effective business management and improving the quality of customer service.

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Sun, Zhaohao. "Data, Analytics, and Intelligence." Journal of Computer Science Research 5, no. 4 (2023): 43–57. http://dx.doi.org/10.30564/jcsr.v5i4.6072.

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We are living in an age of big data, analytics, and artificial intelligence (AI). After reviewing a dozen different books on big data, data analytics, data science, AI, and business intelligence (BI), there are the current questions: 1) What are the relationships between data, analytics, and intelligence? 2) What are the relationships between big data and big data analytics? 3) What is the relationship between BI and data analytics? This article first discusses the heuristics of the Greek philosopher Plato and French mathematician Descartes and how to reshape the world. Then it addresses the above questions based on a Boolean structure, which destructs big data, data analytics, data science, and AI into data, analytics, and intelligence as the Boolean atoms. Data, analytics, and intelligence are reorganized and reassembled, based on the Boolean structure, to data analytics, analytics intelligence, data intelligence, and data analytics intelligence. The research will analyse each of them after examining the system intelligence. The proposed approach in this research might facilitate the research and development of big data, data analytics, AI, and data science.

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Researcher. "Cloud-Based AI and Big Data Analytics for Real-Time Business Decision-Making." International Journal of Finance (IJFIN) 36, no. 6 (2023): 96–123. https://doi.org/10.5281/zenodo.14905134.

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<em>The rising sun of technological development has arrived to illuminate and innovate the traditional business operational processes. Providing academic and practical contributions, this essay explores the effect of cloud-based artificial intelligence and big data analytics on business decision-making. It is observed that cloud-based AI and big data analytics support real-time business decision-making activities. Unlike the traditional business decision support framework, contemporary business decision-support systems depend on different categories of data analysis fields such as artificial intelligence, big data analytics, advanced analytics, and business intelligence. The innovative data analysis process of cloud-based AI and big data analytics is transforming business processes too. The findings are expected to generate new knowledge about the role of contemporary AI and big data analytical tools in business intelligence and to bridge the gap between AI, business intelligence, and big data analytics by investigating the effect of AI and big data analytics on business intelligence environments. Furthermore, it holds the potential to motivate and encourage further studies in utilizing new AI and big data analytical techniques in the field of business decision-making.</em> <em>Real-time decision-making has become a significant aspect of business operations in the era of digitization and the technological evolution of contemporary artificial intelligence, deep learning, and machine learning. The theoretical and industry-oriented analysis of artificial intelligence, big data analytics, and personal learning accurately in the context of cloud computing is lacking. The purpose of this essay is to understand the effect of cloud-based AI and big data analytics on business decision-making. The findings of the essay may yield an innovative understanding of groundbreaking AI and personal data analytical techniques in the field of business intelligence and decision-making under complex situations. </em>

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The Lancet Digital Health. "Big data, artificial intelligence, and the opioid crisis." Lancet Digital Health 3, no. 6 (2021): e330. http://dx.doi.org/10.1016/s2589-7500(21)00087-x.

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Benke, Kurt, and Geza Benke. "Artificial Intelligence and Big Data in Public Health." International Journal of Environmental Research and Public Health 15, no. 12 (2018): 2796. http://dx.doi.org/10.3390/ijerph15122796.

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Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.

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Shin, Jeongwon. "Artificial Intelligence and Big Data in Visual Art." Korean Journal of Arts Studies ll, no. 25 (2019): 65–89. http://dx.doi.org/10.20976/kjas.2019..25.004.

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Cho, Sang-A., and Hansang Kim. "Healthcare Big Data-Based Artificial Intelligence Utilization Strategy." Health Insurance Review & Assessment Service Research 1, no. 2 (2021): 196–207. http://dx.doi.org/10.52937/hira.21.1.2.196.

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ÖZENGEN, Mehmet Emre, Ali OKATAN, and Can BALKAYA. "Artificial Intelligence and Big Data in Fraud Detection." EURAS Journal of Engineering and Applied Sciences 1, no. 2 (2021): 63–74. http://dx.doi.org/10.17932/ejeas.2021.024/ejeas_v01i2001.

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Chang, Anthony C. "Big data in medicine: The upcoming artificial intelligence." Progress in Pediatric Cardiology 43 (December 2016): 91–94. http://dx.doi.org/10.1016/j.ppedcard.2016.08.021.

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Ojokoh, Bolanle A., Oluwarotimi W. Samuel, Olatunji M. Omisore, et al. "Big data, analytics and artificial intelligence for sustainability." Scientific African 9 (September 2020): e00551. http://dx.doi.org/10.1016/j.sciaf.2020.e00551.

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Allam, Zaheer, and Zaynah A. Dhunny. "On big data, artificial intelligence and smart cities." Cities 89 (June 2019): 80–91. http://dx.doi.org/10.1016/j.cities.2019.01.032.

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Krittanawong, Chayakrit, Kipp W. Johnson, Steven G. Hershman, and W. H. Wilson Tang. "Big data, artificial intelligence, and cardiovascular precision medicine." Expert Review of Precision Medicine and Drug Development 3, no. 5 (2018): 305–17. http://dx.doi.org/10.1080/23808993.2018.1528871.

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Gavrilova, S. A., and A. V. Zastupov. "Big data and artificial intelligence in industrial enterprises." Problemy sovershenstvovaniya organizatsii proizvodstva i upravleniya promyshlennymi predpriyatiyami: Mezhvuzovskii sbornik nauchnykh trudov, no. 1 (2022): 10–15. http://dx.doi.org/10.46554/op-mie-2022.1-pp.10.

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Wong, Zoie S. Y., Jiaqi Zhou, and Qingpeng Zhang. "Artificial Intelligence for infectious disease Big Data Analytics." Infection, Disease & Health 24, no. 1 (2019): 44–48. http://dx.doi.org/10.1016/j.idh.2018.10.002.

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Zhang, Yudong, Jin Hong, and Shuwen Chen. "Medical Big Data and Artificial Intelligence for Healthcare." Applied Sciences 13, no. 6 (2023): 3745. http://dx.doi.org/10.3390/app13063745.

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Ye, Yufan. "Intelligent Logistics under Artificial Intelligence and Big Data." Advances in Economics, Management and Political Sciences 62, no. 1 (2023): 277–82. http://dx.doi.org/10.54254/2754-1169/62/20231360.

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With the development of science and technology, high-tech technologies such as artificial intelligence and big data are gradually being applied to daily life. Digitalization has become a new driving force for the transformation and upgrading of the logistics industry, and there are many problems in the current logistics market. The logistics industry has developed rapidly, but the problems of low inventory management and transportation efficiency have not been effectively solved. Research in the field of logistics should not be limited to certain aspects, but should take a holistic approach. It is very meaningful to effectively combine existing advanced technology with existing logistics, strengthen the scientific management of employees, and improve logistics efficiency. This article elaborates on the basic concept of intelligent logistics and analyzes the advantages of applying technology to logistics through scientific analysis methods, combining advanced technology with logistics can effectively avoid the shortcomings of traditional logistics, and improve management efficiency and service quality, hopes to explore and provide suggestions for the development of logistics and intelligent logistics.

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Pei, Yan, and Jijiang Yang. "Biomedical Applications of Big Data and Artificial Intelligence." Bioengineering 12, no. 2 (2025): 207. https://doi.org/10.3390/bioengineering12020207.

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This Special Issue of Bioengineering is dedicated to the profound impact of big data and artificial intelligence (AI) in the fields of biomedical research and healthcare. In an age defined by the rapid evolution of technology, this Issue explores the dynamic intersection of AI and data science with medicine. A total of 14 papers were accepted after a thorough review process, with their topics including disease diagnosis, medical data analysis, image processing, personalized medicine, pathological image segmentation, survival prediction, cognitive load assessment, and medical knowledge extraction. These studies aim to enhance medical image analysis, signal processing, data prediction, and interpretability to improve diagnostic accuracy, medical efficiency, and personalized treatment plans for patients. We hope the publication of this Special Issue can offer a comprehensive view of the transformative power of these innovative approaches and enrich research and investigations into the applications of big data and AI in biomedical research and healthcare.

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Van Harmelen, Frank, James A. Hendler, Pascal Hitzler, and Krzysztof Janowicz. "Semantics for Big Data." AI Magazine 36, no. 1 (2015): 3–4. http://dx.doi.org/10.1609/aimag.v36i1.2559.

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This editorial introduction summarizes the seven guest-edited contributions to AI Magazine that explore opportunities and challenges arising from transferring and adapting semantic web technologies to the big data quest.

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Supriyanto, Eko Eddya, Hardi Warsono, and Augusin Rina Herawati. "Literature Study on the Use of Big Data and Artificial Intelligence in Policy Making in Indonesia." Administratio: Jurnal Ilmiah Administrasi Publik dan Pembangunan 12, no. 2 (2021): 139–53. http://dx.doi.org/10.23960/administratio.v12i2.235.

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The use of big data and artificial intelligence in decision-making in Indonesia is still rarely implemented. But in the business world, big data and artificial intelligence are very commonplace to boost targets. This study discusses the use of big data and artificial intelligence in policy Making in Indonesia. The method used in this paper is qualitative research with a literature study approach. The result of this research is that the dynamics in the implementation of public services require appropriate and fast decision making, considering that this is a community demand. Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly. In conclusion, big data and artificial intelligence can help public leaders make decisions to deliver the best policies. This research implies that it can be used as a reference for policymakers that big data and artificial intelligence can be used in decision-making to warn Policymaking.

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OKORO, Godsday Edesiri PhD, Ifeyinwa Victoria OWAMAH, and Precious Ogechukwuka OBIEKEA. "ARTIFICIAL INTELLIGENCE (AI), BIG-DATA ANALYTICS, AND THE VALUE RELEVANCE OF ACCOUNTING INFORMATION." ISIR Journal of Business and Management Studies (ISIRJBMS) 2, no. 3 (2025): 01–04. https://doi.org/10.5281/zenodo.15429663.

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<em>Value-relevance of accounting information studies are more often than not hinged on the idea that stakeholders adjust their actions and respond swiftly by changing the value of shares when they get pertinent information. </em><em>The study investigates the relationship between artificial intelligence, big-data analytics and value-relevance of accounting information.  Questionnaire was the main instrument of data collection, which was administered to 188 respondents. Data obtained were analyzed using descriptive, diagnostic and inferential statistical techniques. The multiple regression results revealed that artificial intelligence and big-data analytic can lead to increase in value-relevance of accounting information; thus, artificial intelligence and big-data analytics could predict financial information.  T</em><em>he study contributes to knowledge in accounting and management in general by establishing that when artificial intelligence and big-data analytics are employed by firms, it could lead to increase in the value-relevance of accounting information.  On the basis of this, </em><em>artificial intelligence and big-data analytics could allow appreciative, better and robust understanding of financial information and as a way of predicting financial information; hence artificial intelligence and big-data analytics usage should be encouraged. </em> <strong><em>JEL Classification: </em></strong><em>M41; M40</em> <strong><em>Acknowledgement: </em></strong><em>We wish to acknowledge the Tertiary Education Trust Fund (TETFUND) for the Research Grant given to the authors (Reference No. TETF/DR&D/UNI/ABRAKA/IBR/2021/VOL.I).</em>

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