Bibliografías temáticas / Data-driven techniques

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Literatura académica sobre el tema "Data-driven techniques"

Autor: Grafiati

Publicado: 30 de noviembre de 2024

Última modificación: 5 de julio de 2025

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Artículos de revistas sobre el tema "Data-driven techniques"

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 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 (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-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, 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 & 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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Tesis sobre el tema "Data-driven techniques"

Mousas, Christos. "Data-driven techniques for animating virtual characters." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/52967/.

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One of the key goals of current research in data-driven computer animation is the synthesis of new motion sequences from existing motion data. This thesis presents three novel techniques for synthesising the motion of a virtual character from existing motion data and develops a framework of solutions to key character animation problems. The first motion synthesis technique presented is based on the character's locomotion composition process. This technique examines the ability of synthesising a variety of character's locomotion behaviours while easily specified constraints (footprints) are placed in the three-dimensional space. This is achieved by analysing existing motion data, and by assigning the locomotion behaviour transition process to transition graphs that are responsible for providing information about this process. However, virtual characters should also be able to animate according to different style variations. Therefore, a second technique to synthesise real-time style variations of character's motion. A novel technique is developed that uses correlation between two different motion styles, and by assigning the motion synthesis process to a parameterised maximum a posteriori (MAP) framework retrieves the desire style content of the input motion in real-time, enhancing the realism of the new synthesised motion sequence. The third technique presents the ability to synthesise the motion of the character's fingers either o↵-line or in real-time during the performance capture process. The advantage of both techniques is their ability to assign the motion searching process to motion features. The presented technique is able to estimate and synthesise a valid motion of the character's fingers, enhancing the realism of the input motion. To conclude, this thesis demonstrates that these three novel techniques combine in to a framework that enables the realistic synthesis of virtual character movements, eliminating the post processing, as well as enabling fast synthesis of the required motion.

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Battle, Leilani Marie. "Behavior-driven optimization techniques for scalable data exploration." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111853.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 153-162). Interactive visualizations are a popular medium used by scientists to explore, analyze and generally make sense of their data. However, with the overwhelming amounts of data that scientists collect from various instruments (e.g., telescopes, satellites, gene sequencers and field sensors), they need ways of efficiently transforming their data into interactive visualizations. Though a variety of visualization tools exist to help people make sense of their data, these tools often rely on database management systems (or DBMSs) for data processing and storage; and unfortunately, DBMSs fail to process the data fast enough to support a fluid, interactive visualization experience. This thesis blends optimization techniques from databases and methodology from HCI and visualization in order to support interactive and iterative exploration of large datasets. Our main goal is to reduce latency in visualization systems, i.e., the time these systems spend responding to a user's actions. We demonstrate through a comprehensive user study that latency has a clear (negative) effect on users' high-level analysis strategies, which becomes more pronounced as the latency is increased. Furthermore, we find that users are more susceptible to the effects of system latency when they have existing domain knowledge, a common scenario for data scientists. We then developed a visual exploration system called Sculpin that utilizes a suite of optimizations to reduce system latency. Sculpin learns user exploration patterns automatically, and exploits these patterns to pre-fetch data ahead of users as they explore. We then combine data-prefetching with incremental data processing (i.e., incremental materialization) and visualization-focused caching optimizations to further boost performance. With all three of these techniques (pre-fetching, caching, and pre-computation), Sculpin is able to: create visualizations 380% faster and respond to user interactions 88% faster than existing visualization systems, while also using less than one third of the space required by other systems to store materialized query results. by Leilani Battle. Ph. D.

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Massey, Tammara. "Data driven and optimization techniques for mobile health systems." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1930907801&sid=4&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Nordahl, Christian. "Data-Driven Techniques for Modeling and Analysis of User Behavior." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18667.

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Our society is becoming more digitalized for each day. Now, we are able to gather data from individual users with higher resolution than ever. With the increased amount of data on an individual user level, we can analyze their behavior. This is of interest in many different domains, for example service providers wanting to improve their service for their customers. If they know how their service is used, they have more insight in how they can improve. But, it also imposes additional difficulties. When we reach the individual user, the irregularities in the regular behavior makes it harder to model the normal behavior. In this thesis, we explore data-driven techniques to model and analyze user behaviors. We aim to evaluate existing as well as develop novel technologies to identify approaches that are suitable for use on an individual user level. We use both supervised and unsupervised learning methods to model the user behavior and evaluate the approaches on real world electricity consumption data. Firstly, we analyze household electricity consumption data and investigate the use of regression to model the household's behavior. We identify consumption trends, how data granularity affects modeling, and we show that regression is a viable approach to model user behavior. Secondly, we use clustering analysis to profile individual households in terms of their electricity consumption. We compare two dissimilarity measures, how they affect the clustering analysis, and we investigate how the produced clustering solutions differ. Thirdly, we propose a sequential clustering algorithm to model evolving user behavior. We evaluate the proposed algorithm on electricity consumption data and show how the produced model can be used to identify and trace changes in the user's behavior. The algorithm is robust to evolving behaviors and handles both dynamic and incremental aspects of streaming data.

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Ogweno, Austin Juma. "Power efficient, event driven data acquisition and processing using asynchronous techniques." Thesis, University of Newcastle upon Tyne, 2018. http://hdl.handle.net/10443/4121.

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Data acquisition systems used in remote environmental monitoring equipment and biological sensor nodes rely on limited energy supply soured from either energy harvesters or battery to perform their functions. Among the building blocks of these systems are power hungry Analogue to Digital Converters and Digital Signal Processors which acquire and process samples at predetermined rates regardless of the monitored signal's behavior. In this work we investigate power efficient event driven data acquisition and processing techniques by implementing an asynchronous ADC and an event driven power gated Finite Impulse Response (FIR) filter. We present an event driven single slope ADC capable of generating asynchronous digital samples based on the input signal's rate of change. It utilizes a rate of change detection circuit known as the slope detector to determine at what point the input signal is to be sampled. After a sample has been obtained it's absolute voltage value is time encoded and passed on to a Time to Digital Converter (TDC) as part of a pulse stream. The resulting digital samples generated by the TDC are produced at a rate that exhibits the same rate of change profile as that of the input signal. The ADC is realized in 0.35mm CMOS process, covers a silicon area of 340mm by 218mm and consumes power based on the input signal's frequency. The samples from the ADC are asynchronous in nature and exhibit random time periods between adjacent samples. In order to process such asynchronous samples we present a FIR filter that is able to successfully operate on the samples and produce the desired result. The filter also poses the ability to turn itself off in-between samples that have longer sample periods in effect saving power in the process.

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Essaidi, Moez. "Model-Driven Data Warehouse and its Automation Using Machine Learning Techniques." Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_essaidi.pdf.

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L'objectif de ce travail de thèse est de proposer une approche permettant l'automatisation complète du processus de transformation de modèles pour le développement d'entrepôts de données. L'idée principale est de réduire au mieux l'intervention des experts humains en utilisant les traces de transformations réalisées sur des projets similaires. L'objectif est d'utiliser des techniques d'apprentissage supervisées pour traiter les définitions de concepts avec le même niveau d'expression que les données manipulées. La nature des données manipulées nous a conduits à choisir les langages relationnels pour la description des exemples et des hypothèses. Ces langages ont l'avantage d'être expressifs en donnant la possibilité d'exprimer les relations entres les objets manipulés mais présente l'inconvénient majeur de ne pas disposer d'algorithmes permettant le passage à l'échelle pour des applications industrielles. Pour résoudre ce problème, nous avons proposé une architecture permettant d'exploiter au mieux les connaissances issues des invariants de transformations entre modèles et métamodèles. Cette manière de procéder a mis en lumière des dépendances entre les concepts à apprendre et nous a conduits à proposer un paradigme d'apprentissage dit de concepts-dépendants. Enfin, cette thèse présente plusieurs aspects qui peuvent influencer la prochaine génération de plates-formes décisionnelles. Elle propose, en particulier, une architecture de déploiement pour la business intelligence en tant que service basée sur les normes industrielles et les technologies les plus récentes et les plus prometteuses This thesis aims at proposing an end-to-end approach which allows the automation of the process of model transformations for the development of data warehousing components. The main idea is to reduce as much as possible the intervention of human experts by using once again the traces of transformations produced on similar projects. The goal is to use supervised learning techniques to handle concept definitions with the same expressive level as manipulated data. The nature of the manipulated data leads us to choose relational languages for the description of examples and hypothesises. These languages have the advantage of being expressive by giving the possibility to express relationships between the manipulated objects, but they have the major disadvantage of not having algorithms allowing the application on large scales of industrial applications. To solve this problem, we have proposed an architecture that allows the perfect exploitation of the knowledge obtained from transformations' invariants between models and metamodels. This way of proceeding has highlighted the dependencies between the concepts to learn and has led us to propose a learning paradigm, called dependent-concept learning. Finally, this thesis presents various aspects that may inuence the next generation of data warehousing platforms. The latter suggests, in particular, an architecture for business intelligence-as-a-service based on the most recent and promising industrial standards and technologies

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Stender, Merten [Verfasser]. "Data-driven techniques for the nonlinear dynamics of mechanical structures / Merten Stender." Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2020. http://d-nb.info/1221669583/34.

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Godwin, Jamie Leigh. "Exploiting robust multivariate statistics and data driven techniques for prognosis and health management." Thesis, Durham University, 2015. http://etheses.dur.ac.uk/11157/.

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This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96).

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Fields, Evan(Evan Jerome). "Demand uncensored : car-sharing mobility services using data-driven and simulation-based techniques." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121825.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 141-145). In the design and operation of urban mobility systems, it is often desirable to understand patterns in traveler demand. However, demand is typically unobserved and must be estimated from available data. To address this disconnect, we begin by proposing a method for recovering an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation technique based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information about the unknown distribution is available but the truncation process can be simulated. The proposed method is compared to the ubiquitous maximum likelihood (MLE) method in a variety of synthetic validation experiments where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slight misspecified MLE. We then describe a novel car-sharing simulator which captures many of the important interactions between supply, demand, and system utilization while remaining simple and computationally efficient. In collaboration with Zipcar, a leading car-sharing operator in the United States, we demonstrate the usefulness of our detruncation method combined with our simulator via a pair of case studies. These tools allow us to estimate demand for round trip car-sharing services in the Boston and New York metropolitan areas, and the inferred demand distributions contain actionable insights. Finally, we extend the detruncation method to cover cases where data is noisy, missing, or must be combined from different sources such as web or mobile applications. In synthetic validation experiments, the extended method is benchmarked against kernel density estimation (KDE) with Gaussian kernels. We find that the proposed method typically outperforms KDE, especially when the distribution to be estimated is not unimodal. With this extended method we consider the added utility of search data when estimating demand for car-sharing. by Evan Fields. Ph. D. Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

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Reinoso, Nicholas L. "Forecasting Harmful Algal Blooms for Western Lake Erie using Data Driven Machine Learning Techniques." Cleveland State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=csu1494343783463819.

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Libros sobre el tema "Data-driven techniques"

Damper, Robert I., ed. Data-Driven Techniques in Speech Synthesis. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3413-3.

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Damper, R. I. Data-Driven Techniques in Speech Synthesis. Springer US, 2001.

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I, Damper R., ed. Data-driven techniques in speech synthesis. Kluwer Academic Publishers, 2001.

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Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. Data-Driven Remaining Useful Life Prognosis Techniques. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5.

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Kumar Rana, Arun, Sumit Kumar Rana, Vishnu Sharma, and Ritu Dewan. Intelligent Data-Driven Techniques for Security of Digital Assets. CRC Press, 2025. https://doi.org/10.1201/9781003590439.

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P, Ford Michael, and Erekson James A, eds. Accessible assessment: How 9 sensible techniques can power data-driven reading instruction. Heinemann, 2011.

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W, Stoughton John, Mielke Roland R, and Langley Research Center, eds. Strategies for concurrent processing of complex algorithms in data driven architectures. National Aeronautics and Space Administration, Langley Research Center, 1990.

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Data-Driven Techniques in Speech Synthesis. Island Press, 2001.

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Winston, Wayne L. Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley & Sons, Incorporated, John, 2014.

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Winston, Wayne L. Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley & Sons, Incorporated, John, 2014.

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Capítulos de libros sobre el tema "Data-driven techniques"

Stolper, Charles D., Bongshin Lee, Nathalie Henry Riche, and John Stasko. "Data-Driven Storytelling Techniques." In Data-Driven Storytelling. A K Peters/CRC Press, 2018. http://dx.doi.org/10.1201/9781315281575-4.

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Hasan Hussain, S., T. B. Sivakumar, and Alex Khang. "Cryptocurrency Methodologies and Techniques." In The Data-Driven Blockchain Ecosystem. CRC Press, 2022. http://dx.doi.org/10.1201/9781003269281-2.

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Jansen, Bernard J., Joni Salminen, Soon-gyo Jung, and Kathleen Guan. "Using Data-Driven Personas Alongside Other Human-Computer Interaction (HCI) Techniques." In Data-Driven Personas. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02231-9_8.

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Courty, Nicolas, and Thomas Corpetti. "Data-Driven Animation of Crowds." In Computer Vision/Computer Graphics Collaboration Techniques. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71457-6_34.

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Ateeq, Muhammad, and Muhammad Khalil Afzal. "Programming Languages, Tools, and Techniques." In Data-Driven Intelligence in Wireless Networks. CRC Press, 2023. http://dx.doi.org/10.1201/9781003216971-13.

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Liang, Yuan, Song-Hai Zhang, and Ralph Robert Martin. "Automatic Data-Driven Room Design Generation." In Next Generation Computer Animation Techniques. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69487-0_10.

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Livraga, Giovanni. "Privacy in Microdata Release: Challenges, Techniques, and Approaches." In Data-Driven Policy Impact Evaluation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78461-8_5.

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Singh, Sanika, Aman Anand, Tanupriya Choudhury, Pankaj Sharma, and Ved P. Mishra. "Extensive Review on Product Recommendation Techniques." In Data Driven Approach Towards Disruptive Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9873-9_43.

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Guru, Sudhanshu Kumar, and Lov Kumar. "Investigation of Predictive Power of Sentiment Analysis Model Developed Using Different Word Embedding Techniques." In Data-Driven Decision Making. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2902-9_2.

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Rani, Komal, Naveen Kumar, Manju Rani, and Kamal. "Intelligent data mining techniques." In Intelligent Data-Driven Techniques for Security of Digital Assets. CRC Press, 2025. https://doi.org/10.1201/9781003590439-8.

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Actas de conferencias sobre el tema "Data-driven techniques"

Hsieh, Wen-Chun, Yu-Ting Sheng, and Shih-Yuan Wang. "Exploration of Incremental Sheet Forming for Application in Formwork Techniques." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.085.

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Dangwal, Kartikeye, Iman Ardekani, Soheil Varastehpour, and Abdolhossein Sarrafzadeh. "Bioacoustic Data Augmentation Techniques for Data-Driven Apiculture." In 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS). IEEE, 2024. https://doi.org/10.1109/fmlds63805.2024.00031.

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Mansuri, Ahmad, Asterios Agkathidis, Davide Lombardi, and Hanmei Chen. "Rethinking Bamboo Roof-Based Architecture of Indonesian Traditional House Using Parametric Design and Automated Fabrication Techniques." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.203.

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Tsurunaga, Shinya, Tomohiro Fukuda, and Nobuyoshi Yabuki. "Enhanced Landscape Visualization of Post-Structure Removal: Integrating 3D reconstruction techniques and diffusion models through machine learning." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.549.

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Zhang, Guodong, Zhuo Jin, Tianyu Yao, and Jiawei Qin. "ECET: Enhanced Vulnerability Detection through Code Embedding Techniques." In 2024 Sixth International Conference on Next Generation Data-driven Networks (NGDN). IEEE, 2024. http://dx.doi.org/10.1109/ngdn61651.2024.10744066.

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Shinde, Priyanka P., V. P. Desai, and Kavita S. Oza. "A Data Driven Mental Health Analysis Using Machine Learning Techniques." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696584.

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Rao, K. Dhananjay, CH Sunil, N. Bala Adithya, Naga Lakshmi Pujitha Nagasarapu, Ch Manaswi, and Prakash. "Data Driven Techniques for Fault Detection in Lithium Ion Battery." In 2024 International Conference on Sustainable Power & Energy (ICSPE). IEEE, 2024. https://doi.org/10.1109/icspe62629.2024.10924393.

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Fan, Zhiwei, Chao Xu, Yuling Huang, et al. "MonoDDT: Monocular 3D Object Detection Enhanced by Data-Driven Techniques." In 2025 7th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2025. https://doi.org/10.1109/csecs64665.2025.11009909.

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Gabrielski, Jawana, and Ulf Häger. "Advancing Standard Load Profiles with Data-Driven Techniques and Recent Datasets." In 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 2024. http://dx.doi.org/10.1109/smartgridcomm60555.2024.10738046.

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Meng, Yiming, Hangyu Li, Melkior Ornik, and Xiaopeng Li. "Koopman-Based Data-Driven Techniques for Adaptive Cruise Control System Identification." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920220.

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Informes sobre el tema "Data-driven techniques"

Crews, John H., Ralph C. Smith, Kyle M. Pender, Jennifer C. Hannen, and Gregory D. Buckner. Data-driven Techniques to Estimate Parameters in the Homogenized Energy Model for Shape Memory Alloys. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada556936.

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Gravina, Antonio Francesco, and Matteo Lanzafame. “What’s Your Shape?”: A Data-Driven Approach to Estimating the Environmental Kuznets Curve. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps240334-2.

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This paper uses Bayesian Model Averaging techniques to explore the relationship between environmental degradation and economic growth and finds that per capita gross domestic product (GDP) and carbon dioxide emissions are positively associated in emerging economies. Due to model and variable selection uncertainty, studies on the inverted U-shaped relationship between environmental degradation and economic growth—known as the Environmental Kuznets Curve—have produced very mixed evidence. This paper addresses these issues by using Bayesian Model Averaging techniques. The results indicate that the Environmental Kuznets Curve has an inverted-N shape. Almost all of the emerging economies analyzed display a positive association between per capita GDP and carbon dioxide emissions, whereas most advanced economies analyzed are on the second downward segment of the curve.

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Zanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca, and Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005194.

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Hidden populations, such as irregular migrants, often elude traditional probabilistic sampling methods. In situations like these, chain-referral sampling techniques like Respondent-Driven Surveys (RDS) offer an effective solution. RDS, a variant of network sampling sometimes referred to as “snowball” sampling, estimates weights based on the network structures of friends and acquaintances formed during the sampling process. This ensures the samples are representative of the larger population. However, one significant limitation of these methods is the rigidity of the weights. When faced with participant attrition, recalibrating these weights to ensure continued representation poses a challenge. This technical note introduces a straightforward methodology to account for such attrition. Its applicability is demonstrated through a survey targeting Venezuelan migrants in Ecuador and Peru.

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Hu, Zhengzheng, Ralph C. Smith, and Jon Ernstberger. The Homogenized Energy Model (HEM) for Characterizing Polarization and Strains in Hysteretic Ferroelectric Materials: Implementation Algorithms and Data-Driven Parameter Estimation Techniques. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada556961.

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Chhipi-Shrestha, Gyan, Vipul Moudgil, Rachid Ouche, et al. Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769705.

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).

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Mohammadian, Abolfazl, Amir Bahador Parsa, Homa Taghipour, Amir Davatgari, and Motahare Mohammadi. Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-033.

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Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it addresses critical ethical considerations, including data privacy, transparency in AI-driven decisions, and reducing algorithmic bias to ensure fair, trustworthy, and responsible marketing practices. Looking ahead, this Article highlights emerging trends like hyper-personalization, ethical AI frameworks, and the integration of AI with technologies like AR/VR and IoT, offering a forward-looking perspective on AI's role in shaping consumer-centric and ethical digital marketing. Keywords: Artificial intelligence, digital marketing, personalized content, ethical advertising, consumer behavior, recommendation engines, predictive analytics, natural language processing, data privacy, transparency, algorithmic bias, hyper-personalization, responsible marketing, AR/VR, IoT, consumer engagement.

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Rose and Luo. L52069 Guided Wave Sizing and Discrimination for SCC Magnetostriction ILI Inspection. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0011179.

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With the goal to be able to inspect a large area of the pipe from a single sensor position, hence the development of what we call guided wave inspection. From one point on the circumference of the pipe, it is therefore proposed to inspect the entire circumferential area of the pipe. This approach leads to inspection efficiency.� Experimental techniques cannot be developed on a trial and error basis. The purpose of this work is therefore to develop a guided wave propagation model and subsequent numerical solutions to assist in the �theoretically driven� sensor design and data acquisition systems for improved nondestructive testing including defect detection, location, and potential classification and sizing analysis in a pipe.

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Lindig, Sascha, Julien Deckx, Magnus Herz, et al. Technical Key Performance Indicators for Photovoltaic Systems: Challenges and Best Practices. International Energy Agency Photovoltaic Power Systems Programme, 2024. https://doi.org/10.69766/luzi3108.

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Key Performance Indicators (KPIs) are an important set of metrics used to assess various aspects of photovoltaic (PV) systems, including their long-term performance, economic viability and carbon footprint. Technical KPIs support data-driven and informed decision-making when optimizing PV systems and provide a comprehensive overview of how PV systems operate across different conditions and climates. Different KPIs are commonly employed throughout the entire value chain of PV projects and can be categorized into technical, economic and sustainability aspects. In this work, a set of best practices for handling PV system data to reliably calculate relevant KPIs is discussed. While most technical KPIs are generally well-known among asset owners, EPCs, O&M providers and consultants, not all stakeholders in the financing-to-operation chain are equally aware of the nuances and consequences of certain decisions, which are based on how technical KPIs are operationalized, i.e. translated from contracts to how and where raw data are stored, which data cleaning and imputation techniques are used, to how the technical KPIs are calculated and used for subsequent decision-making. In many cases, the decisions made in the development-to-construction phase, will affect the asset for a significant part of its lifetime. For example, the resolution at which data is measured, which data are stored, or whether data back-ups are on-site or in the cloud, can all affect how KPIs are calculated, affect future modifications to contractual clauses, or the need for SCADA upgrades. Hence, this work aims to provide all stakeholders deeper insights and a shared understanding of the most important technical KPIs. The work is divided into three parts, each addressing different aspects of KPIs, data management, and their mapping potential

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