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Journal articles on the topic 'Data-driven techniques'

Author: Grafiati

Published: 30 November 2024

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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 (May 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 (August 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 determine the equation of a polynomial function through two or more points that model the graph of “good fit” for a set of data. Using these techniques, students can develop functions through which they can evaluate mathematical behavior and make predictions. Secondary mathematics teachers will find these techniques particularly valuable. Each technique can be applied within various secondary mathematics courses such as algebra 2, statistics, or precalculus.

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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 (April 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 (January 31, 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-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

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Li, Tao, Ning Xie, Chunqiu Zeng, Wubai Zhou, Li Zheng, Yexi Jiang, Yimin Yang, et al. "Data-Driven Techniques in Disaster Information Management." ACM Computing Surveys 50, no. 1 (April 13, 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 (January 2013): 40–49. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000597.

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Li, Tao, Chunqiu Zeng, Yexi Jiang, Wubai Zhou, Liang Tang, Zheng Liu, and Yue Huang. "Data-Driven Techniques in Computing System Management." ACM Computing Surveys 50, no. 3 (October 9, 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 (March 15, 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 (March 31, 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 & granularity can be applied in the data processing. Granularity will process the data from low granularity to high granularity. Sampling plays an important role in the data processing.

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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 (February 16, 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 (August 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 (December 2018): 241–53. http://dx.doi.org/10.1016/j.ijtst.2018.09.001.

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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 (July 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. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.

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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 (March 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 (July 13, 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 pharmaceutical processes, based on the mathematical and algorithmic principles behind the modeling methods. Different statistical tools, such as multivariate tools, Bayesian inferences, and machine learning approaches, i.e., unsupervised learning, supervised learning (including deep learning) and reinforcement learning, are presented. Various applications in the pharmaceutical processes, as well as the connections from statistics and machine learning methods, are discussed in the narrative procedures of introducing different types of data-driven models. Afterwards, two case studies, including dynamic reaction data modeling and catalyst-kinetics prediction of cross-coupling reactions, are presented to illustrate the power and advantages of different data-driven models. We also discussed current challenges and future perspectives of data-driven modeling methods, emphasizing the integration of data-driven and mechanistic models, as well as multi-scale modeling.

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Carpenter, Chris. "Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization." Journal of Petroleum Technology 73, no. 10 (October 1, 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 not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

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Ferreira, Anselmo, Luca Bondi, Luca Baroffio, Paolo Bestagini, Jiwu Huang, Jefersson A. dos Santos, Stefano Tubaro, and Anderson Rocha. "Data-Driven Feature Characterization Techniques for Laser Printer Attribution." IEEE Transactions on Information Forensics and Security 12, no. 8 (August 2017): 1860–73. http://dx.doi.org/10.1109/tifs.2017.2692722.

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19

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 (May 28, 2019): 3451–62. http://dx.doi.org/10.15244/pjoes/93923.

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20

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 (February 2018): 295–314. http://dx.doi.org/10.1109/taes.2017.2756518.

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21

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 (July 2000): 59–64. http://dx.doi.org/10.1016/s1474-6670(17)39603-9.

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22

Matwin, Stan, Luca Tesei, and Roberto Trasarti. "Computational modelling and data-driven techniques for systems analysis." Journal of Intelligent Information Systems 52, no. 3 (April 11, 2019): 473–75. http://dx.doi.org/10.1007/s10844-019-00554-z.

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23

Hardy, Hilda, Alan Biermann, R. Bryce Inouye, Ashley McKenzie, Tomek Strzalkowski, Cristian Ursu, Nick Webb, and Min Wu. "The Amitiés system: Data-driven techniques for automated dialogue." Speech Communication 48, no. 3-4 (March 2006): 354–73. http://dx.doi.org/10.1016/j.specom.2005.07.006.

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24

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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25

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 (October 11, 2011): 457–74. http://dx.doi.org/10.1007/s11269-011-9926-7.

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26

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 (July 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 better classification in processing the data provided by the sensors. Finally, an evaluation process is performed to detect blade damage. The results show that the proposed RMSE metric performs at an ideal level, making it a promising strategy for the detection of blade damage.

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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 (November 8, 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 the overall academic performance of students, curriculum coherence, dropout paths and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

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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 (June 30, 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 interactive storytelling game. Results show that our drama manager can significantly increase the likelihood of the drama manager's desired story continuation.

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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 (August 19, 2017): 1449–61. http://dx.doi.org/10.1007/s40808-017-0367-z.

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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 (October 1, 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 unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.

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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 (September 12, 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 and key performance indicators (KPIs) such as revenue growth, cost reduction, and customer satisfaction. A comprehensive mixed-methods approach was employed. Qualitative data was collected through interviews with executives from 15 companies across different sectors. Quantitative data was obtained through surveys distributed to 25 organizations. Statistical analysis, including correlation and regression analysis, was conducted to identify patterns and relationships. Our analysis reveals a strong positive correlation between the adoption of data-driven strategies and business performance metrics. On average, companies that embraced data-driven decision-making experienced a 20% increase in revenue, a 15% reduction in operational costs, and a 10% improvement in customer satisfaction compared to those that did not. This study underscores the transformative potential of data-driven strategies in contemporary business environments. Organizations that leverage data effectively not only enhance their financial performance but also better meet customer expectations. We conclude that data-driven decision-making is no longer a luxury but a strategic imperative for businesses looking to thrive in the digital age.

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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 (August 31, 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, and their working strategies to understand the role of directors and producers in the age of the digital era in film-making. The research aims to find answers to the capabilities of data-driven movie-making techniques and, accordingly, it makes a number of predictions about the role of human beings in the production of an artwork and analyses the role of the software. The research also investigates the pros and cons of using big data in the film-making industry. Keywords: Artificial intelligence, cinema, data mining, film-making.

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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 (February 15, 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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Londhe, Shreenivas, and Shrikant Charhate. "Comparison of data-driven modelling techniques for river flow forecasting." Hydrological Sciences Journal 55, no. 7 (October 12, 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 (November 14, 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 (February 1, 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 helper relies on a physical analysis and a k-nearest neighbours classifier using the Fréchet distance to score the measurements. This approach allows to measure the similarity between curves, independently from sampling. The training phase is performed on a labelled dataset created from actual impedance tube measurements and possibly some computer generated results to bridge gaps. The inputs are frequency-dependent quantities including normal sound absorption curves, surface impedance, dynamic mass density and dynamic bulk modulus.

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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 (April 2021): 27–38. http://dx.doi.org/10.70589/jrtcse.2021.1.3.

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Jiang, Lili, and Vicenç Torra. "Data Protection and Multi-Database Data-Driven Models." Future Internet 15, no. 3 (February 27, 2023): 93. http://dx.doi.org/10.3390/fi15030093.

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Anonymization and data masking have effects on data-driven models. Different anonymization methods have been developed to provide a good trade-off between privacy guarantees and data utility. Nevertheless, the effects of data protection (e.g., data microaggregation and noise addition) on data integration and on data-driven models (e.g., machine learning models) built from these data are not known. In this paper, we study how data protection affects data integration, and the corresponding effects on the results of machine learning models built from the outcome of the data integration process. The experimental results show that the levels of protection that prevent proper database integration do not affect machine learning models that learn from the integrated database to the same degree. Concretely, our preliminary analysis and experiments show that data protection techniques have a lower level of impact on data integration than on machine learning models.

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Shukla, Varun, and Pradip Patil. "Enhancing Customer Sales Prediction through Advanced Data Visualization Techniques: A Data-Driven Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (October 5, 2024): 1–14. http://dx.doi.org/10.55041/ijsrem37680.

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This research paper examines advanced techniques used in visualizing data in regard to improved sales prediction by the customers within an enterprise. The data-driven decision-making process is explored better through the enhancing of the accuracy of sales forecast and actual understanding of customers' behaviour. There are steps begun with data cleaning to create accuracy and reliability. This is then followed by detailed analysis on trends, patterns, and perhaps sales drivers. The study unfolds the importance of visual tools, dashboards, charts, and graphs, that translate complex analytical results into clear, actionable insights. Visualizations enable key findings to be absorbed quickly by stakeholders, thereby making for more informed and strategic decisions. Business organizations can effectively communicate insights and strategies to optimize sales performance with advanced tools like Tableau and Power BI. It indicates that firms are able to find new markets by using data visualizations, enhance customer buying behavior by understanding them better, and improve the decision-making process as well. The precision of sales forecasts increases when advanced visual tools are used in making these forecasts, in turn helping the organization remain competitive in the marketplace. The paper thus provides a direction for business organizations seeking to adopt data analysis and visualization in their sales strategy as a way of increasing customer engagement for better revenue growth. Keywords – Data Collection, Data cleaning, Data analysis, Data visualization, Segmentation

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Quarshie, Paul, and Temitope Isaiah Asefon. "Data-Driven Techniques and Data Analytics in Water Treatment Facilities: Innovative Safety Protocols and Optimization." Journal of Geography, Environment and Earth Science International 28, no. 10 (September 24, 2024): 65–77. http://dx.doi.org/10.9734/jgeesi/2024/v28i10826.

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Aim: To examine harnessing of data-driven techniques and data analytics in water treatment facilities considering innovative safety protocols and process optimization. Problem Statement: The most significant part of human existence and industrial operations has been linked to water which often becomes vulnerable to hazardous contaminants which are brought on through natural processes and human activity. Significance of Study: The use of water treatment facilities in order to make water a sustainable natural resource for the whole world is vital. To achieve this, it is imperative to monitor and classify water quality. Methodology: Recent literature materials in form of books, journals and relevant published articles in the area of data-driven techniques and data analytics in water treatment facilities were consulted. Discussion: This review article has critically examined harnessing of data-driven and data analytics in water treatment facilities with consideration also given to innovative safety protocols and process optimization. The basic fundamental principle of water treatment facilities were discussed alongside the application of data-driven techniques. Safety protocols in water treatment facilities were discussed. Data driven techniques in water treatment facilities and applications of machine learning and artificial intelligence techniques to water treatment were explained. It was noticed that AI-Driven solutions transforms real-time water quality analysis and offers a proactive approach to water treatment. The integration of machine learning and AI algorithms capabilities in water treatment facilities can now respond quickly to fluctuations and continuously monitor key parameters fluctuations in water quality. Consideration was given to artificial intelligence as data analytics optimization tool for water treatment facilities. Future prospects, limitations and challenges of the study were stated. Conclusion: AI algorithms and historical data can be harnessed into water treatment facilities in order to assess performance patterns, predict equipment malfunctions and proactively plan the maintenance activities.

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Yauck, Mamadou, Erica EM Moodie, Herak Apelian, Alain Fourmigue, Daniel Grace, Trevor Hart, Gilles Lambert, and Joseph Cox. "General regression methods for respondent-driven sampling data." Statistical Methods in Medical Research 30, no. 9 (July 28, 2021): 2105–18. http://dx.doi.org/10.1177/09622802211032713.

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Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals’ social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.

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Morabito, Niccolò, Erik Quaeghebeur, and Laurens Bliek. "Investigating data-driven surrogates for the Ainslie wake model." Journal of Physics: Conference Series 2767, no. 8 (June 1, 2024): 082002. http://dx.doi.org/10.1088/1742-6596/2767/8/082002.

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Abstract Efficient and accurate calculation of wind turbine wakes are central to the design and analysis of wind farms. We present the promising results of an investigation into machine learning techniques for creating computationally efficient surrogates of the Ainslie wake model, as a computationally accessible stepping stone to more complex models. We have compiled an extensive dataset comprising 8600 wake fields, and explored a diverse range of techniques. Among these, the most promising techniques are regression decision trees (RDTs) and Fourier-expanded multi-layer perceptrons (FEMLPs) which have proven to be very effective for image applications. Even with smaller training sets, acceptable results are achieved, offering a feasible balance between size and performance. Both RDTs and FEMLPs excel in interpolation, while FEMLPs particularly showcase robust extrapolation capabilities. Visual comparisons show that RDTs outperform FEMLPs in terms of the smoothness of the generated wake fields. All techniques create surrogates that are at least six orders of magnitude faster than the Ainslie model implementation. The investigation results in a flexible pair of turbine wake surrogate models which are extremely fast and achieve high performance with a reasonable training time. Their generic nature makes them promising candidates for creating effective surrogates for more complex wake models.

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Wang, Tiexin, Yi Wu, Jacques Lamothe, Frederick Benaben, Ruofan Wang, and Wenjing Liu. "A Data-Driven and Knowledge-Driven Method towards the IRP of Modern Logistics." Wireless Communications and Mobile Computing 2021 (March 11, 2021): 1–15. http://dx.doi.org/10.1155/2021/6625758.

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Inventory Routing Problem (IRP) is a typical optimization problem in logistics. To reduce the total cost, which contains the product transportation cost, the inventory holding cost, the customer satisfaction cost, etc., a wide range of impact factors have to be taken into consideration. Since more and more intelligent devices have been adopted in the management of modern logistics, the amount of the collected data (relevant to those impact factors) increases exponentially. However, the quality of the collected data is suffering from a certain number of uncertainties, such as device status and the transmission network environment. Considering the volume and quality of the collected data, the traditional data-driven distribution optimization methods encounter a bottleneck. In this paper, we propose a hybrid optimization method which combines data-driven and knowledge-driven techniques together. In our method, a domain ontology, which has better scalability and generality, is built as an extension of data-driven optimization algorithms. Knowledge reasoning techniques are also combined to handle data quality issue and uncertainties. To evaluate the performance of our method, we carried out a case study, which is provided by a French company “Pierre Fabre Dermo-Cosmetics” (PFDC). This case study is a simplified scenario of the practical business process of PFDC.

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Wang, Haoyu, Liu Hong, and Leonardo P. Chamorro. "Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods." Micromachines 15, no. 5 (May 8, 2024): 629. http://dx.doi.org/10.3390/mi15050629.

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Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

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Schwartz, H. Andrew, and Lyle H. Ungar. "Data-Driven Content Analysis of Social Media." ANNALS of the American Academy of Political and Social Science 659, no. 1 (April 9, 2015): 78–94. http://dx.doi.org/10.1177/0002716215569197.

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Researchers have long measured people’s thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. The proliferation of social media, such as Twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. We describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective well-being, health, gender differences, and personality.

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