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Journal articles on the topic 'Data-driven solutions'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Megha, Megha, Rananjay Parth Ghai, Tisha Gandhi, and Rajveer Singh. "AI-Driven Diagnostics: Bridging the Gap Between Data and Healthcare Solutions." International Journal of Research Publication and Reviews 6, sp5 (2025): 88–93. https://doi.org/10.55248/gengpi.6.sp525.1912.

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Coleman, Peter T. "Data-driven solutions to U.S. polarization." Science 371, no. 6526 (2021): 245.1–245. http://dx.doi.org/10.1126/science.abg1730.

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Sasmal, Shubhodip. "Data Warehousing Revolution: AI-driven Solutions." INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES 12, no. 1 (2024): 01–06. http://dx.doi.org/10.55083/irjeas.2024.v12i01001.

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The contemporary landscape of data warehousing is undergoing a revolutionary transformation propelled by the integration of Artificial Intelligence (AI). This paper explores the intersection of AI and data warehousing, unraveling the dynamics that fuel this revolution and examining the profound implications for businesses and data management practices. The traditional paradigm of data warehousing has relied on structured data and predefined schemas, limiting its adaptability to the dynamic nature of modern datasets. The advent of AI injects a new dimension, enabling data warehouses to evolve i

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Chowdhery, Syed Azad, and Marco Bertoni. "Data-driven value assessment of packaging solutions." IFAC-PapersOnLine 51, no. 11 (2018): 1119–24. http://dx.doi.org/10.1016/j.ifacol.2018.08.452.

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Joseph, Bellal, Letitia Bible, and Kamil Hanna. "Developing Data-Driven Solutions to Firearm Violence." Current Trauma Reports 6, no. 1 (2020): 44–50. http://dx.doi.org/10.1007/s40719-020-00185-8.

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Chintala, Suman. "Harnessing AI and BI for Smart Cities: Transforming Urban Life with Data Driven Solutions." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 337–42. http://dx.doi.org/10.21275/sr24902235715.

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7

Researcher. "AUTONOMOUS DATA HEALING: AI-DRIVEN SOLUTIONS FOR ENTERPRISE DATA INTEGRITY." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 20–32. https://doi.org/10.5281/zenodo.14044978.

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This comprehensive article explores the emerging field of autonomous data healing systems, which leverage advanced AI techniques to address the growing challenges of data quality and integrity in the era of big data. The article examines the system architecture, self-optimization capabilities, and real-world applications of these AI-driven solutions across various industries. Through detailed case studies and aggregate results from multiple implementations, the research demonstrates significant improvements in data quality, operational efficiency, and cost savings achieved by autonomous data h

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Facklam, Ferdinand, and Felipe Pecegueiro do Amaral Curado. "Data driven parametric design." PARC Pesquisa em Arquitetura e Construção 2, no. 7 (2011): 49. http://dx.doi.org/10.20396/parc.v2i7.8634582.

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The focus of this paper is that we want to give a brief introduction about the idea of Parametric Design (PD) and the use of data to inform the design process. The digital fabrication is not covered in detail in this document. In the case study “Live Building” explains a sensory process. The project shows how to collect data, transformed and transported into a shape. Innovation is not only the approach of the draft, but the systematic procedure and the resulting diversity of solutions. The search for the geometric shape and the key to the concept will be answered in detail.

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Pérez-Paredes, Pascual, and Jose M. Alcaraz-Calero. "Developing annotation solutions for online Data Driven Learning." ReCALL 21, no. 1 (2009): 55–75. http://dx.doi.org/10.1017/s0958344009000093.

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AbstractAlthough annotation is a widely-researched topic in Corpus Linguistics (CL), its potential role in Data Driven Learning (DDL) has not been addressed in depth by Foreign Language Teaching (FLT) practitioners. Furthermore, most of the research in the use of DDL methods pays little attention to annotation in the design and implementation of corpus-based/driven language teaching.In this paper, we set out to examine the process of development of SACODEYL Annotator, an application that seeks to assist SACODEYL system users in annotating XML multilingual corpora. First, we discuss the role of

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Lavanya Jacintha Victor. "Fleet telematics: Transforming transportation through data-driven solutions." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 3970–76. https://doi.org/10.30574/wjarr.2025.26.1.1568.

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This article presents a comprehensive overview of fleet telematics technology and its transformative impact on transportation management across various sectors. Fleet telematics integrates GPS tracking, onboard diagnostics, sensor technology, and wireless communication systems to create an interconnected data ecosystem that revolutionizes vehicle and driver management. The article explores the core technological infrastructure powering these systems, including their location tracking capabilities, diagnostic interfaces, and data transmission mechanisms. It examines how telematics enables opera

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Luciano, Ruth G. "Enhancing church donation management using data-driven solutions." International Journal of ADVANCED AND APPLIED SCIENCES 12, no. 2 (2025): 118–25. https://doi.org/10.21833/ijaas.2025.02.013.

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Efficient management of church donations is essential yet challenging for religious institutions due to inefficiencies and lack of transparency in traditional manual methods. This study developed a Church Donation Management System (CDMS) for a Cathedral in Cabanatuan City, utilizing the Agile Software Development Life Cycle (SDLC) to incorporate iterative improvements based on stakeholder feedback. The CDMS features an automated donor database, customizable reports, and real-time analytics to streamline donation tracking and enhance financial oversight. Evaluated using ISO/IEC 25010 Software

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Pan, Yinghui, Jing Tang, Biyang Ma, Yifeng Zeng, and Zhong Ming. "Toward data-driven solutions to interactive dynamic influence diagrams." Knowledge and Information Systems 63, no. 9 (2021): 2431–53. http://dx.doi.org/10.1007/s10115-021-01600-5.

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AbstractWith the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general fram

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Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (2019): 135–40. http://dx.doi.org/10.1556/1848.2019.0016.

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Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which tr

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Sakagianni, Aikaterini, Christina Koufopoulou, Petros Koufopoulos, et al. "Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions." Antibiotics 13, no. 11 (2024): 1052. http://dx.doi.org/10.3390/antibiotics13111052.

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Background/Objectives: The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. It aims to determine the patterns from AMR gene data that are clinically relevant and, in public health, capable of informing

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Hema Madhavi Kommula. "The Evolution of Data-Driven Supply Chain Finance Solutions." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 458–66. https://doi.org/10.32996/jcsts.2025.7.2.47.

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The evolution of supply chain finance has entered a transformative phase driven by data analytics, artificial intelligence, and digital technologies. This article examines how these innovations are reshaping financial relationships between buyers, suppliers, and financial institutions in supply chains worldwide. It explores the core data technologies revolutionizing supply chain finance, including ERP integration, advanced analytics, and automated financing platforms. The article further investigates how artificial intelligence and machine learning applications enhance credit risk assessment,

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Debjyoti, Dutta1 Nidhi1* Geeta Kumari2. "Smart Farming: Data Driven Solutions for a Sustainable Future." Trends in Agriculture Science 3, no. 1 (2025): 2426–30. https://doi.org/10.5281/zenodo.14837405.

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Modern agriculture faces the challenge of feeding a growing population while minimizing environmental impact. This paper explores the integration of precision farming, protected cultivation, and advanced statistical analysis as a pathway to sustainable and efficient food production. Precision farming leverages technologies like GPS and remote sensing to tailor agricultural practices to individual fields, optimizing resource use and minimizing environmental pollution. Protected cultivation enhances crop quality and year-round production by providing controlled growing environments. Statistics p

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Aigner, Kevin-Martin, Marc Goerigk, Michael Hartisch, Frauke Liers, and Arthur Miehlich. "A Framework for Data-Driven Explainability in Mathematical Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 20912–20. http://dx.doi.org/10.1609/aaai.v38i19.30081.

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Advancements in mathematical programming have made it possible to efficiently tackle large-scale real-world problems that were deemed intractable just a few decades ago. However, provably optimal solutions may not be accepted due to the perception of optimization software as a black box. Although well understood by scientists, this lacks easy accessibility for practitioners. Hence, we advocate for introducing the explainability of a solution as another evaluation criterion, next to its objective value, which enables us to find trade-off solutions between these two criteria. Explainability is a

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Naga, Santhosh Reddy Vootukuri. "AI-Driven Unified Framework for Mental Health Data." International Journal in Engineering Sciences 2, no. 4 (2025): 1–8. https://doi.org/10.5281/zenodo.15179470.

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Integrating Artificial Intelligence (AI) into the mental health field is revolutionizing the profession, with greater accuracy of diagnosis and room for customized intervention therapies. The traditional practice of mental healthcare through personal opinions and reactive treatments with resulting late diagnoses is replaced by data-driven, scalable solutions provided by AI-based platforms. This paper is a continuation of Chavali's (2024)[1] research on a "Unified Data Integration and Record Identification Framework" and suggests a revised framework adapted for mental health data. Building on A

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Inglot, Tadeusz. "Data driven efficient score tests for Poissonity." Probability and Mathematical Statistics 39, no. 1 (2019): 115–26. http://dx.doi.org/10.19195/0208-4147.39.1.8.

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New data driven score tests for testing goodness of fit of the Poisson distribution are proposed. They are direct applications of the general construction of data driven goodness-of-fit tests for composite hypotheses developed in Inglot et al. 1997. By a simulation study it is shown that these tests perform almost equally well as the best known solutions for standard alternatives and outperform them for more difficult alternatives.

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Li, Zhuoyang. "Big Data Management: Empowering Sustainable Logistics with Data-Driven Operation Optimization." Advances in Economics, Management and Political Sciences 54, no. 1 (2023): 64–68. http://dx.doi.org/10.54254/2754-1169/54/20230878.

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Numerous disciplines, including logistics, have experienced a paradigm shift since the advent of big data. As the backbone of many industries, logistics has been significantly impacted by the expansion of big data. The purpose of this paper is to investigate the far-reaching effects of big data technology on the development of sustainable logistics, with a particular emphasis on the role of big data in various parts of the logistics process. Through a literature review and case analysis, this paper focuses on summarizing the application of big data in all aspects of logistics, and selecting an

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Researcher. "HOW AI IMPROVES DATA QUALITY FOR BETTER BUSINESS INSIGHTS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 590–601. https://doi.org/10.5281/zenodo.14197959.

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In today's rapidly evolving digital landscape, organizations face unprecedented challenges in managing and maintaining data quality across their enterprise systems. This comprehensive article explores how Artificial Intelligence (AI) revolutionizes data quality management, offering sophisticated data cleaning, validation, and enrichment solutions. The article examines the current state of data quality challenges, presents AI-powered solutions, and demonstrates their impact through real-world applications in the financial services and manufacturing sectors. Through detailed case studies and ind

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22

Arefin, Sabira, and Mia Simcox. "AI-Driven Solutions for Safeguarding Healthcare Data: Innovations in Cybersecurity." International Business Research 17, no. 6 (2024): 74. https://doi.org/10.5539/ibr.v17n6p74.

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AI in healthcare data security is a significant development since healthcare is progressively leaning towards electronic health records, telemedicine, and mobile health apps. These technologies have greatly enhanced the quality of patient care and operation efficiency. However, at the same time, they have also assumed considerable hazards by opening up patients&#39; information to cybercriminals. It is also noteworthy that healthcare organizations are especially attractive to hackers since they work with personal, financial, and medical data. While initial levels of cyber defense include f

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Anestis, Michael D., Allison E. Bond, and Shelby L. Bandel. "Understanding Risk and Implementing Data-Driven Solutions for Firearm Suicide." ANNALS of the American Academy of Political and Social Science 704, no. 1 (2022): 204–22. http://dx.doi.org/10.1177/00027162231173321.

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Each year, firearms account for half of all the suicide deaths in the U.S. Research has shown that, worldwide, the most effective way to prevent suicide is so-called means safety: making the tools and methods of suicide less accessible and less lethal. In the U.S., research has shown, time and again, that access to firearms increases the risk for suicide death, particularly when firearms are not stored safely. Means safety, therefore, could be a powerful tool in reducing suicide deaths in America, where firearms are highly lethal, widely available, and frequently used within a specific geograp

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Malipeddi, Anil Kumar. "Revolutionizing Data Center Cooling: Aerodynamics Solutions for AI-Driven Workloads." Journal of Artificial Intelligence, Machine Learning and Data Science 2, no. 2 (2024): 1953–55. https://doi.org/10.51219/jaimld/anil-kumar-malipeddi/429.

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Canady, Valerie A. "Data‐driven benchmarks can improve patient MH outcomes." Mental Health Weekly 33, no. 41 (2023): 3–5. http://dx.doi.org/10.1002/mhw.33830.

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Observing that the mental health care industry has traditionally lacked technology solutions to track data to measure and improve the effectiveness of treatment over time, a technology platform has generated a data‐driven “blueprint” for clinicians to help inform treatment decisions at the point of care.

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Li, Yuliang, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. "Data augmentation for ML-driven data preparation and integration." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3182–85. http://dx.doi.org/10.14778/3476311.3476403.

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In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning.

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Korie, Izuchukwu Josephmartin, Chudi-Ajabor Ogochukwu, and Onwuagba Kenechi Innocent. "Reservoir Geomechanics: A Data-driven Approach." Journal of Energy Research and Reviews 15, no. 2 (2023): 47–56. http://dx.doi.org/10.9734/jenrr/2023/v15i2306.

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Reservoir geomechanics is a crucial aspect of optimising and developing oil and gas activities, especially in maximising production. Recent technological advancements have revolutionised reservoir geomechanics studies, including integrating data-driven approaches. This review examines and integrates machine learning, data science, and data twin in reservoir studies. The primary aim is to identify the benefits, limitations, significant advancements, potential challenges, opportunities, and research gaps of data-driven approaches to reservoir geomechanics. Additionally, this study aims to create

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Khair, Mohammad Afghanul, Nusrat Shaheen, Mohammed Misbahul Khair, and Shalu Jain. "HCM AI Agents and Data-Driven Decision Making." International Journal of Research in Modern Engineering & Emerging Technology 13, no. 3 (2025): 36–54. https://doi.org/10.63345/ijrmeet.org.v13.i3.3.

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In the modern enterprise landscape, Human Capital Management (HCM) is undergoing a paradigm shift, leveraging artificial intelligence (AI) agents and data-driven decision-making. AI-powered HCM solutions optimize talent acquisition, workforce planning, and employee engagement by harnessing machine learning, natural language processing, and predictive analytics. These intelligent agents enhance efficiency by automating repetitive HR tasks, such as recruitment screening, performance evaluations, and employee sentiment analysis. Furthermore, data-driven insights empower HR leaders to make strateg

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Li, Xi, Zehua Wang, Victor C. M. Leung, Hong Ji, Yiming Liu, and Heli Zhang. "Blockchain-empowered Data-driven Networks." ACM Computing Surveys 54, no. 3 (2021): 1–38. http://dx.doi.org/10.1145/3446373.

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The paths leading to future networks are pointing towards a data-driven paradigm to better cater to the explosive growth of mobile services as well as the increasing heterogeneity of mobile devices, many of which generate and consume large volumes and variety of data. These paths are also hampered by significant challenges in terms of security, privacy, services provisioning, and network management. Blockchain, which is a technology for building distributed ledgers that provide an immutable log of transactions recorded in a distributed network, has become prominent recently as the underlying t

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Devarasetty, Narendra. "Transforming Data Warehousing with AI-Driven Innovations in Data Engineering." International Journal of Scientific Research and Management (IJSRM) 9, no. 02 (2021): 575–87. https://doi.org/10.18535/ijsrm/v9i2.ec02.

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Data warehousing has been identified as an important component of business intelligence because it provides the infrastructure that organisational databases require in order to support the storage, management and analysis of large volumes of data. However, with the exponential growth of data and the increasing complexity of the analytical workloads, conventional data warehousing systems may fail to address the modern business needs of most organizations. The adoption of AI in data engineering is therefore a revolutionary opportunity for resolving these challenges as it provides novel solutions

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Abrar Ahmed Syed. "Implementing a Data-Driven Culture in Organizations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1276–84. https://doi.org/10.32628/cseit251112123.

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Implementing a data-driven culture in organizations represents a transformative journey that fundamentally reshapes operational paradigms and decision-making processes. This article explores the essential components of data-driven transformation, including data governance frameworks, technical infrastructure requirements, and data literacy programs. The article examines leadership's crucial role in driving cultural change, addresses common implementation challenges, and presents effective solutions for organizations undertaking this transformation. Through detailed examination of success metri

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Wang, Jiaying. "Research on Project-driven Data Structure Teaching Reform." International Journal of Education and Humanities 11, no. 3 (2023): 4–6. http://dx.doi.org/10.54097/ijeh.v11i3.14433.

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This paper proposes relevant solutions to the current problems in data structure teaching design. Introducing project-driven teaching methods into data structure courses, relying on actual projects, enables students to master data structure knowledge during the process of completing projects, and achieve the teaching purpose of data structure. Teaching practice has shown that project-driven teaching methods not only effectively overcome the drawbacks of traditional teaching methods, but also enhance students' comprehensive abilities and cultivate their comprehensive qualities.

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Benjelloun, Sarah, Mohamed El Mehdi El Aissi, Younes Lakhrissi, and Safae El Haj Ben Ali. "Data Lake Architecture for Smart Fish Farming Data-Driven Strategy." Applied System Innovation 6, no. 1 (2023): 8. http://dx.doi.org/10.3390/asi6010008.

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Thanks to continuously evolving data management solutions, data-driven strategies are considered the main success factor in many domains. These strategies consider data as the backbone, allowing advanced data analytics. However, in the agricultural field, and especially in fish farming, data-driven strategies have yet to be widely adopted. This research paper aims to demystify the situation of the fish farming domain in general by shedding light on big data generated in fish farms. The purpose is to propose a dedicated data lake functional architecture and extend it to a technical architecture

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Radziszewska, Aleksandra. "Data-Driven Approach in Knowledge-Based Smart City Management." European Conference on Knowledge Management 24, no. 2 (2023): 1090–98. http://dx.doi.org/10.34190/eckm.24.2.1600.

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The concept of smart city management is based on the implementation and use of advanced technologies, such as wireless sensors, intelligent vehicles, mobile networks, and data storage technologies. It involves integrating various information and communication technology solutions to efficiently manage a city's resources. Cities are investing in data-driven smart technologies to enhance performance and efficiency, thereby generating a large amount of data. Finding innovative ways to use this data helps improve city management and urban development. A data-driven city utilizes datafication to op

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Czvetkó, Tímea, Alex Kummer, Tamás Ruppert, and János Abonyi. "Data-driven business process management-based development of Industry 4.0 solutions." CIRP Journal of Manufacturing Science and Technology 36 (January 2022): 117–32. http://dx.doi.org/10.1016/j.cirpj.2021.12.002.

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Keyantuo, Patrick, Ruiting Wang, Teng Zeng, Aashrith Vishwanath, Hoseinali Borhan, and Scott J. Moura. "Distributionally Robust and Data-Driven Solutions to Commercial Vehicle Routing Problems." IFAC-PapersOnLine 56, no. 2 (2023): 10497–502. http://dx.doi.org/10.1016/j.ifacol.2023.10.1069.

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Crawford, Broderick, Ricardo Soto, José Lemus-Romani, et al. "Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics." Mathematics 9, no. 16 (2021): 1839. http://dx.doi.org/10.3390/math9161839.

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One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating tha

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Sukhanov, Ivan, Anna Volkova, Ahmet Kose, Allan Hani, and Eduard Petlenkov. "Challenges, benefits, and open questions in Data-Driven Commercial Building Cooling Optimization." E3S Web of Conferences 562 (2024): 06003. http://dx.doi.org/10.1051/e3sconf/202456206003.

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The rising demand for building cooling presents a challenge, expected to triple energy usage by 2050. European Parliament targets a 40% reduction in final energy and 42.5% in primary energy by 2030. This paper examines data-driven solutions in Building Management Systems (BMS) for energy-efficient cooling. It outlines challenges, benefits, and open questions in Data-Driven Commercial Building Cooling Optimization. By integrating data-driven techniques with current infrastructure, it proposes sustainable cooling practices. Results indicate potential energy reductions of up to 24%, lower costs,

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Glynn, Calina, and Jose A. Rodriguez. "Data-driven challenges and opportunities in crystallography." Emerging Topics in Life Sciences 3, no. 4 (2019): 423–32. http://dx.doi.org/10.1042/etls20180177.

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Abstract Structural biology is in the midst of a revolution fueled by faster and more powerful instruments capable of delivering orders of magnitude more data than their predecessors. This increased pace in data gathering introduces new experimental and computational challenges, frustrating real-time processing and interpretation of data and requiring long-term solutions for data archival and retrieval. This combination of challenges and opportunities is driving the exploration of new areas of structural biology, including studies of macromolecular dynamics and the investigation of molecular e

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Bisola Beatrice Oguejiofor, Adedolapo Omotosho, Kehinde Mobolaji Abioye, et al. "A REVIEW ON DATA-DRIVEN REGULATORY COMPLIANCE IN NIGERIA." International Journal of Applied Research in Social Sciences 5, no. 8 (2023): 231–43. http://dx.doi.org/10.51594/ijarss.v5i8.571.

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In Nigeria's dynamic regulatory landscape, compliance challenges pose formidable obstacles to businesses and organizations across various sectors. This research explores Nigeria's multifaceted world of compliance, highlighting the intricacies of regulatory requirements, bureaucratic complexities, and resource constraints that organizations face. Amid these challenges, the pivotal role of data-driven approaches and technology in enhancing compliance efforts emerges as a central theme. The study's key findings reveal the complexity of Nigeria's regulatory environment, characterized by multiple a

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FIALKOWSKI, Valkiria, Aguinaldo SANTOS, and Maria Fernanda ANDREATTA. "DATA-DRIVEN DESIGN PARA O COMPORTAMENTO SUSTENTÁVEL: SETUP DA INTELIGÊNCIA." IMPACT projects 1, no. 2 (2023): 127–39. http://dx.doi.org/10.59279/impact.v1i2.2076.

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Generating sustainable products and services that meet market expectations is a challenge. In this scenario, intelligence based on Big Data has gained prominence by enabling the rapid acquisition of information about user behavior. This intelligence can make it possible to make more assertive decisions for the development of design solutions. Therefore, the objective of this article is to present a critical, exploratory and qualitative analysis of the intelligence setup for the DDD (Data-Driven Design) aiming to influence sustainable behavior. As a method, a bibliographic survey was adopted fo

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Melas, Evangelos, Costas Poulios, Elias Camouzis, John Leventides, and Nick Poulios. "Study of the Hypergeometric Equation via Data Driven Koopman-EDMD Theory." Axioms 12, no. 2 (2023): 134. http://dx.doi.org/10.3390/axioms12020134.

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We consider a data-driven method, which combines Koopman operator theory with Extended Dynamic Mode Decomposition. We apply this method to the hypergeometric equation which is the Fuchsian equation with three regular singular points. The space of solutions at any of its singular points is a two-dimensional linear vector space on the field of reals when the independent variable is restricted to take values in the real axis and the unknown function is restricted to be a real-valued function of a real variable. A basis of the linear vector space of solutions is spanned by the hypergeometric funct

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Mêda, Pedro, Diego Calvetti, Eilif Hjelseth, and Hipólito Sousa. "Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction." Buildings 11, no. 11 (2021): 554. http://dx.doi.org/10.3390/buildings11110554.

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The construction industry faces multiple challenges, where transition to circular production is key. Digitalisation is a strategy to increase the sector’s productivity, competitiveness, and efficiency. However, digitalisation also impacts environmental goals, such as those concerning more eco-friendly solutions, energy efficiency, products recycling, and sustainability certifications. These strategies rely on data, understood as digital, interoperable, incremental and traceable. Data related concepts, such as digital data templates (DDT) and digital building logbooks (DBL), contribute to “good

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Relich, Marcin. "A Data-Driven Approach for Improving Sustainable Product Development." Sustainability 15, no. 8 (2023): 6736. http://dx.doi.org/10.3390/su15086736.

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A product’s impact on environmental issues in its complete life cycle is significantly determined by decisions taken during product development. Thus, it is of vital importance to integrate a sustainability perspective in methods and tools for product development. The paper aims at the development of a method based on a data-driven approach, which is dedicated to identifying opportunities for improving product sustainability at the design stage. The proposed method consists of two main parts: predictive analytics and simulations. Predictive analytics use parametric models to identify relations

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Katam, Brahma Reddy. "Data + AI Driven Solutions for Enhancing Employee Wellbeing and Work-Life Balance." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 08 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem36975.

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This paper investigates the potential of AI-driven solutions to monitor and enhance employee wellbeing and work-life balance by leveraging data such as login hours, number of team meetings, total working days in a year, and vacation days taken, companies can provide personalized wellness recommendations. This approach aims to improve employee health, reduce absenteeism, and boost productivity, ultimately enhancing organizational performance. Key Words: AI-driven solutions, employee wellbeing, work- life balance, workplace activity data, login hours, team meetings, vacation days, productivity,

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Singh, Alok. "Optimizing Retail Operations with AI-Driven Data Analytics." British Journal of Marketing Studies 13, no. 3 (2025): 72–84. https://doi.org/10.37745/bjms.2013/vol13n37284.

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This comprehensive article explores the transformative impact of artificial intelligence on retail operations, with a focus on data-driven strategies that optimize business performance. The article examines how AI technologies are revolutionizing inventory management through advanced demand forecasting and stock level optimization while enhancing customer experiences via personalized recommendations and targeted marketing initiatives. By investigating implementation methodologies, technological requirements, and potential challenges, the article provides retail executives and technology leader

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Shubham Sharma, Tanuja Sharma, Akanchha Tiwari, and Surbhi Gupta. "Streamlining IoT-driven Data Using Blockchain." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1509–14. http://dx.doi.org/10.47392/irjaem.2024.0204.

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The integration of Internet of Things (IoT) technology has revolutionized data collection and analytics, creating vast networks of interconnected devices generating large volumes of data. However, this rapid expansion of IoT ecosystems introduces significant challenges related to data security, integrity, scalability, and privacy. Blockchain technology, with its decentralized and immutable nature, emerges as a compelling solution to address these challenges. This paper explores the synergy between IoT and blockchain, examining how blockchain can streamline IoT-driven data by providing secure d

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Gonnade, Priyanka, and Sonali Ridhorkar. "Data Driven Decision making Framework for Businesses." Journal of Neonatal Surgery 14, no. 6S (2025): 648–60. https://doi.org/10.52783/jns.v14.2301.

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Digital technologies have revolutionized the way businesses are built and managed, requiring the development of new solutions and a diverse set of applications. Massive volumes of data are now easily accessible and database capacity has risen tremendously and data collection methods have altered. As a result, while mining big data, issues with regression, the analytical process, and the complexity of the large data all arise.To cope with the aforementioned issues a data analysis framework is proposed for various business decision-making processes which collects the data and saves in Hadoop, a

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Researcher. "AI-DRIVEN DATA INTEGRATIONS AND AUTOMATIONS FOR CLINICAL RESEARCH OPERATIONS (CRO)." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 2140–49. https://doi.org/10.5281/zenodo.14348930.

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The complexity of modern clinical research operations (CRO) necessitates advanced solutions for managing vast datasets, streamlining workflows, and ensuring compliance with stringent regulatory requirements. AI-driven data integration and automation technologies have emerged as key enablers of efficiency and accuracy in CRO, facilitating real-time data synchronization, workflow automation, and predictive analytics. This comprehensive article examines the current challenges in clinical research operations, explores AI-driven solutions for data integration, evaluates automation technologies

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Đurović, Zorica, and Milena Dževerdanović-Pejović. "Student Perception on Data-Driven Learning." Časopis Pomorskog fakulteta Kotor - Journal of Maritime Sciences 24, no. 2 (2023): 78–90. http://dx.doi.org/10.56080/jms231106.

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With the boosting of information technologies, language instructors and subject teachers have been provided with new opportunities in designing teaching materials and methods. Electronic corpora and available software solutions also provide easier access to professional material, both for teachers and their students. We, therefore, sought to explore the possibilities to utilize contemporary corpus linguistics methods in teaching technical vocabulary. Specifically, we conducted research with Marine Engineering students at the Faculty of Maritime Studies Kotor, University of Montenegro, during o

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