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Artykuły w czasopismach na temat „Data-driven techniques”

Autor: Grafiati

Data publikacji: 30 listopada 2024

Data aktualizacji: 31 lipca 2025

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1

Kumar, Sandeep. "Enhancing Data Privacy in SAP Finance with Artificial Intelligence Driven Masking Techniques." International Journal of Science and Research (IJSR) 13, no. 5 (2024): 1819–24. http://dx.doi.org/10.21275/sr24518072929.

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Bossé, Michael J. "Data-Driven Mathematics Investigations on Curved Data." Mathematics Teacher 99, no. 1 (2005): 46–54. http://dx.doi.org/10.5951/mt.99.1.0046.

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Investigations of real–world data begin in elementary school. Students often produce scatter plots, leading to trend lines. In the middle grades, lines of best fit are often investigated through median–median lines and double–centroid lines (Shawer et al. 2002). In the secondary grades, linear regression is produced by the least squares line. While these techniques are adequate for data that is more or less linear, teachers and students often encounter data that produce a “curved” scatter plot. In these cases additional techniques are required. This article demonstrates three techniques to det

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Azkune, Gorka, Aitor Almeida, Diego López-de-Ipiña, and Liming Chen. "Extending knowledge-driven activity models through data-driven learning techniques." Expert Systems with Applications 42, no. 6 (2015): 3115–28. http://dx.doi.org/10.1016/j.eswa.2014.11.063.

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Zhong, Jinghui, Dongrui Li, Zhixing Huang, Chengyu Lu, and Wentong Cai. "Data-driven Crowd Modeling Techniques: A Survey." ACM Transactions on Modeling and Computer Simulation 32, no. 1 (2022): 1–33. http://dx.doi.org/10.1145/3481299.

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Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-d

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Li, Tao, Ning Xie, Chunqiu Zeng, et al. "Data-Driven Techniques in Disaster Information Management." ACM Computing Surveys 50, no. 1 (2017): 1–45. http://dx.doi.org/10.1145/3017678.

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Arunkumar, R., and V. Jothiprakash. "Reservoir Evaporation Prediction Using Data-Driven Techniques." Journal of Hydrologic Engineering 18, no. 1 (2013): 40–49. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000597.

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Li, Tao, Chunqiu Zeng, Yexi Jiang, et al. "Data-Driven Techniques in Computing System Management." ACM Computing Surveys 50, no. 3 (2017): 1–43. http://dx.doi.org/10.1145/3092697.

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I., V. "Data Engineering: using Data Analysis Techniques in Producing Data Driven Products." International Journal of Computer Applications 161, no. 1 (2017): 13–16. http://dx.doi.org/10.5120/ijca2017912712.

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Meliboev, Azizjon. "ANALYZING HOTEL DATA-DRIVEN SYSTEM BY USING DATA SCIENCE TECHNIQUES." QO‘QON UNIVERSITETI XABARNOMASI 11 (June 30, 2024): 108–11. http://dx.doi.org/10.54613/ku.v11i11.971.

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In the past few years, both the City Hotel and Resort Hotel have experienced significant increases in their cancellation rates. As a result, both hotels are currently facing a range of challenges, such as reduced revenue and underutilized hotel rooms. Therefore, the top priority for both hotels is to reduce their cancellation rates, which will enhance their efficiency in generating revenue. This report focuses on the analysis of hotel booking cancellations and other factors that do not directly impact their business and annual revenue generation.

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Poonia, Ramesh Chandra, and Santosh R. Durugkar. "Sampling Techniques Used in Big-Data Driven Applications." Journal of Intelligent Systems and Computing 2, no. 1 (2021): 17–20. http://dx.doi.org/10.51682/jiscom.00201004.2021.

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Data-driven systems process the data from various sources in multiple applications. Data retrieved from heterogeneous sources need to be available in an aggregate and unique format. This requirement gives rise to the process of the Big-data and proposed next-generation big-data processing systems. There are many applications based on contextual data useful for identifying the traffic intensity, changing users per application, weather conditions etc., and serve as next- generation business-specific systems. In such systems data abstraction and representation are the important tasks & granul

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Londhe, Shreenivas, and Gauri Panse-Aglave. "Modelling Stage–Discharge Relationship using Data-Driven Techniques." ISH Journal of Hydraulic Engineering 21, no. 2 (2015): 207–15. http://dx.doi.org/10.1080/09715010.2015.1007092.

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Garg, Vaibhav, and V. Jothiprakash. "Evaluation of reservoir sedimentation using data driven techniques." Applied Soft Computing 13, no. 8 (2013): 3567–81. http://dx.doi.org/10.1016/j.asoc.2013.04.019.

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Kouskoulis, George, Ioanna Spyropoulou, and Constantinos Antoniou. "Pedestrian simulation: Theoretical models vs. data driven techniques." International Journal of Transportation Science and Technology 7, no. 4 (2018): 241–53. http://dx.doi.org/10.1016/j.ijtst.2018.09.001.

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Arpasat, Poohridate, and Wichian Premchaiswadi. "Data-Driven Business Process Improvement." Progress in Applied Science and Technology 14, no. 3 (2024): 11–21. https://doi.org/10.60101/past.2024.256277.

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This research presents an analytical method to improve organizational workflow efficiency by utilizing data from the organization's information system, which was recorded as event logs from a hospital's outpatient department. Through the application of Process Mining techniques using the Disco tool and Fuzzy Miner algorithm, we created a process model for efficiency analysis. The research results demonstrated the effectiveness of the proposed method in analyzing outpatient service processes involving 12,836 patients, which revealed 4,293 distinct process variants. This diversity reflects the c

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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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Dethlefs, Nina. "Context-Sensitive Natural Language Generation: From Knowledge-Driven to Data-Driven Techniques." Language and Linguistics Compass 8, no. 3 (2014): 99–115. http://dx.doi.org/10.1111/lnc3.12067.

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Dong, Yachao, Ting Yang, Yafeng Xing, Jian Du, and Qingwei Meng. "Data-Driven Modeling Methods and Techniques for Pharmaceutical Processes." Processes 11, no. 7 (2023): 2096. http://dx.doi.org/10.3390/pr11072096.

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As one of the most influential industries in public health and the global economy, the pharmaceutical industry is facing multiple challenges in drug research, development and manufacturing. With recent developments in artificial intelligence and machine learning, data-driven modeling methods and techniques have enabled fast and accurate modeling for drug molecular design, retrosynthetic analysis, chemical reaction outcome prediction, manufacturing process optimization, and many other aspects in the pharmaceutical industry. This article provides a review of data-driven methods applied in pharma

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Carpenter, Chris. "Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization." Journal of Petroleum Technology 73, no. 10 (2021): 63–64. http://dx.doi.org/10.2118/1021-0063-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still no

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Ferreira, Anselmo, Luca Bondi, Luca Baroffio, et al. "Data-Driven Feature Characterization Techniques for Laser Printer Attribution." IEEE Transactions on Information Forensics and Security 12, no. 8 (2017): 1860–73. http://dx.doi.org/10.1109/tifs.2017.2692722.

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Üneş, Fatih, Mustafa Demirci, Bestami Taşar, Yunus Kaya, and Hakan Varçin. "Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques." Polish Journal of Environmental Studies 28, no. 5 (2019): 3451–62. http://dx.doi.org/10.15244/pjoes/93923.

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Vehmas, Risto, Juha Jylha, Minna Vaila, Juho Vihonen, and Ari Visa. "Data-Driven Motion Compensation Techniques for Noncooperative ISAR Imaging." IEEE Transactions on Aerospace and Electronic Systems 54, no. 1 (2018): 295–314. http://dx.doi.org/10.1109/taes.2017.2756518.

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Bertolissi, Edy, Mauro Birattari, Gianluca Bontempi, Antoine Duchâteau, and Hugues Bersini. "Data-Driven Techniques for Divide and Conquer Adaptive Control." IFAC Proceedings Volumes 33, no. 16 (2000): 59–64. http://dx.doi.org/10.1016/s1474-6670(17)39603-9.

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Matwin, Stan, Luca Tesei, and Roberto Trasarti. "Computational modelling and data-driven techniques for systems analysis." Journal of Intelligent Information Systems 52, no. 3 (2019): 473–75. http://dx.doi.org/10.1007/s10844-019-00554-z.

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Hardy, Hilda, Alan Biermann, R. Bryce Inouye, et al. "The Amitiés system: Data-driven techniques for automated dialogue." Speech Communication 48, no. 3-4 (2006): 354–73. http://dx.doi.org/10.1016/j.specom.2005.07.006.

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Kaur, Harpreet, and V. Jothiprakash. "Daily precipitation mapping and forecasting using data driven techniques." International Journal of Hydrology Science and Technology 3, no. 4 (2013): 364. http://dx.doi.org/10.1504/ijhst.2013.060337.

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Kisi, Ozgur, Alireza Moghaddam Nia, Mohsen Ghafari Gosheh, Mohammad Reza Jamalizadeh Tajabadi, and Azadeh Ahmadi. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques." Water Resources Management 26, no. 2 (2011): 457–74. http://dx.doi.org/10.1007/s11269-011-9926-7.

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Velasco, D., L. Guzman, B. Puruncajas, C. Tutiven, and Y. Vidal. "Wind turbine blade damage detection using data-driven techniques." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 462–66. http://dx.doi.org/10.24084/repqj21.357.

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This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show

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Tarika, Verma, and S. Gill Nasib. "Machine Learning Techniques for Better Data Driven Decisions Revisited." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 460–64. https://doi.org/10.35940/ijeat.D6766.049420.

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The main goal of machine learning is to accurately predict the decisions to the problems without human expert intervention. These decisions depend upon patterns found and facts learnt during training tenure. However, prior incorporation of human knowledge is necessary for better prediction of the test data. The main aim is to make machines self-reliant for decision making. Providing machine with this vision makes it useful in every modern field. This makes the stepping stone to make computers behave as the humans do. Enhancing its speed and accuracy are the next step in this field. This paper

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Edith A., Ugwu, and Chinonso Joseph Okonkwo. "Predictive Approach for Unemployment Management Using Data-Driven Techniques." International Journal of Research and Innovation in Applied Science X, no. V (2025): 1464–79. https://doi.org/10.51584/ijrias.2025.1005000128.

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This study presents the modelling and implementation of an integrated system for unemployment prediction and employment tracking in Nigeria using data-driven techniques. The unemployment prediction system was initially developed using a linear regression model trained on five years of data from the National Youth Service Corps (NYSC) covering graduates of Higher Education Institutions (HEIs). In parallel, an Employment Tracking System (ETS) was developed using the Feed Forward Neural Network (FFNN) architecture trained using fingerprint data from the Federal Ministry of Labour, Employment, and

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Mendez, Gonzalo, Xavier Ochoa, Katherine Chiluiza, and Bram De Wever. "Curricular Design Analysis: A Data-Driven Perspective." Journal of Learning Analytics 1, no. 3 (2014): 84–119. http://dx.doi.org/10.18608/jla.2014.13.6.

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Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of Learning Analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, course impact on t

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T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standar

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Yu, Hong, and Mark Riedl. "Data-Driven Personalized Drama Management." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 1 (2021): 191–97. http://dx.doi.org/10.1609/aiide.v9i1.12665.

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A drama manager is an omniscient background agent responsible for guiding players through the story space and delivering an enjoyable and coherent experience. Most previous drama managers only consider the designer's intent. We present a drama manager that uses data-driven techniques to model players and provides personalized guidance in the story space without removing player agency. In order to guide players' experiences, our drama manager manipulates the story space to maximize the probability of the players making choices intended by the drama manager. Our system is evaluated on an interac

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Venkataramana, Jaladurgam. "LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS." ICTACT Journal on Soft Computing 15, no. 2 (2024): 3515–22. http://dx.doi.org/10.21917/ijsc.2024.0490.

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The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unifie

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Pandey, P. K., Topi Nyori, and Vanita Pandey. "Estimation of reference evapotranspiration using data driven techniques under limited data conditions." Modeling Earth Systems and Environment 3, no. 4 (2017): 1449–61. http://dx.doi.org/10.1007/s40808-017-0367-z.

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Haland, Christoffer, and Anders Granmo. "Machine Learning for Anomaly Detection: Insights into Data-Driven Applications." International journal of data science and machine learning 05, no. 01 (2025): 36–41. https://doi.org/10.55640/ijdsml-05-01-07.

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Anomaly detection plays a pivotal role in data-driven machine learning applications, enabling the identification of rare or unexpected patterns that deviate from the norm. These anomalies, which can indicate critical events such as fraud, security breaches, equipment failures, or medical conditions, are invaluable in a variety of fields. This paper provides an in-depth review of anomaly analytics, focusing on the various techniques used in machine learning to detect anomalies in complex, high-dimensional data. We explore statistical methods, machine learning-based approaches, and hybrid models

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Hossain, Qaium, Fahmida Yasmin, Tapos Ranjan Biswas, and Nurtaz Begum Asha. "Data-Driven Business Strategies: A Comparative Analysis of Data Science Techniques in Decision-Making." Scholars Journal of Economics, Business and Management 11, no. 09 (2024): 257–63. http://dx.doi.org/10.36347/sjebm.2024.v11i09.002.

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In an era characterized by rapid technological advancements and an explosion of data, businesses are increasingly turning to data-driven strategies to gain a competitive edge. Understanding the effectiveness of such strategies is paramount. This study investigates the impact of data-driven decision-making on business performance in the context of a diverse set of industries. The primary objective of this research is to assess the extent to which data-driven strategies influence business performance. Specifically, we aim to quantify the correlation between the adoption of data-driven approaches

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Beckley, Jessica. "Advanced Risk Assessment Techniques: Merging Data-Driven Analytics with Expert Insights to Navigate Uncertain Decision-Making Processes." International Journal of Research Publication and Reviews 6, no. 3 (2025): 1454–71. https://doi.org/10.55248/gengpi.6.0325.1148.

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Omoruyi, Nosakhare. "Advanced computational methods for financial planning and analysis risk assessment using data science-driven model validation techniques." International Journal of Research Publication and Reviews 6, no. 4 (2025): 3904–18. https://doi.org/10.55248/gengpi.6.0425.1449.

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Bala, Vignesh Charllo. "Efficient Data Harmonization in Distributed Systems: A Scalable Approach for Multi-Site Analytics." Journal of Scientific and Engineering Research 5, no. 7 (2018): 443–49. https://doi.org/10.5281/zenodo.13752722.

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Data harmonization in distributed systems, particularly within distributed environments, presents considerable challenges due to the heterogeneity and geographical dispersion of data sources. This research introduces a scalable and efficient framework designed to integrate, standardize, and cleanse data across diverse sources, thereby ensuring consistency, accuracy, and reliability. The framework employs a multi-phase process that includes advanced data integration and cleaning techniques, combined with the power of distributed computing and parallel processing to efficiently handle large-scal

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Akgülgil Mutlu, Nadide Gizem. "The future of film-making: Data-driven movie-making techniques." Global Journal of Arts Education 10, no. 2 (2020): 167–74. http://dx.doi.org/10.18844/gjae.v10i2.4735.

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Since the term ‘big data’ came to the scene, it has left almost no industry unaffected. Even the art world has taken advantage of the benefits of big data. One of the latest art forms, cinema, eventually started using analytics to predict their audience and their tastes through data mining. In addition to online platforms like Netflix, Amazon Prime and many more, which act on a different basis, the industry itself evolved to a new phase that uses AI in pre-production, production, post-production and distribution phases. This paper researches software, such as Cinelytic, ScriptBook and LargoAI,

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Deb, C., and A. Schlueter. "Review of data-driven energy modelling techniques for building retrofit." Renewable and Sustainable Energy Reviews 144 (July 2021): 110990. http://dx.doi.org/10.1016/j.rser.2021.110990.

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Wan Qi, Woo, Ng Lik Yin, Umaganeswaran Sivaneswaran, and Nishanth G. Chemmangattuvalappil. "A Novel Methodology for Molecular Design via Data Driven Techniques." Journal of Physical Science 28, Suppl. 1 (2017): 1–24. http://dx.doi.org/10.21315/jps2017.28.s1.1.

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Kubiak, Patrick, and Stefan Rass. "An Overview of Data-Driven Techniques for IT-Service-Management." IEEE Access 6 (2018): 63664–88. http://dx.doi.org/10.1109/access.2018.2875975.

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Reddy Kethireddy, Rajashekhar. "AI-Driven Encryption Techniques for Data Security in Cloud Computing." JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING 9, no. 1 (2021): 27–38. http://dx.doi.org/10.70589/jrtcse.2021.1.3.

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Londhe, Shreenivas, and Shrikant Charhate. "Comparison of data-driven modelling techniques for river flow forecasting." Hydrological Sciences Journal 55, no. 7 (2010): 1163–74. http://dx.doi.org/10.1080/02626667.2010.512867.

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Ylioinas, Juha, Norman Poh, Jukka Holappa, and Matti Pietikäinen. "Data-driven techniques for smoothing histograms of local binary patterns." Pattern Recognition 60 (December 2016): 734–47. http://dx.doi.org/10.1016/j.patcog.2016.06.029.

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Kazemi, Pezhman, Christophe Bengoa, Jean-Philippe Steyer, and Jaume Giralt. "Data-driven techniques for fault detection in anaerobic digestion process." Process Safety and Environmental Protection 146 (February 2021): 905–15. http://dx.doi.org/10.1016/j.psep.2020.12.016.

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Shirmohammadi, Bagher, Mehdi Vafakhah, Vahid Moosavi, and Alireza Moghaddamnia. "Application of Several Data-Driven Techniques for Predicting Groundwater Level." Water Resources Management 27, no. 2 (2012): 419–32. http://dx.doi.org/10.1007/s11269-012-0194-y.

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Fu, Daixin, Lingyi Wang, Guanlin Lv, Zhengyu Shen, Hao Zhu, and W. D. Zhu. "Advances in dynamic load identification based on data-driven techniques." Engineering Applications of Artificial Intelligence 126 (November 2023): 106871. http://dx.doi.org/10.1016/j.engappai.2023.106871.

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Gaborit, Mathieu, and Luc Jaouen. "Using data-driven techniques to provide feedback during material characterisation." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 5 (2023): 2305–9. http://dx.doi.org/10.3397/in_2022_0330.

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The aim of the work is to study the feasibility of using machine learning techniques to design a decision helper to assist the characterisation of acoustic materials (porous media for instance). The tool is intended to alert the human operator about specific physical phenomena occurring during the measurements or common mistakes in handling the characterization rig or its parameters. Examples of classical issues include leakage around the samples, unintentional compression during the sample mounting, errors in input parameters such as the static pressure or temperature, etc. The proposed helpe

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