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Relevant bibliographies by topics / Data / features engineering

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Academic literature on the topic 'Data / features engineering'

Author: Grafiati

Published: 25 January 2025

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Journal articles on the topic "Data / features engineering"

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Jadhav, Shailaja B., and D. V. Kodavade. "Enhancing Flight Delay Prediction through Feature Engineering in Machine Learning Classifiers: A Real Time Data Streams Case Study." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (January 31, 2023): 212–18. http://dx.doi.org/10.17762/ijritcc.v11i2s.6064.

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The process of creating and selecting features from raw data to enhance the accuracy of machine learning models is referred to as feature engineering. In the context of real-time data streams, feature engineering becomes particularly important because the data is constantly changing and the model must be able to adapt quickly. A case study of using feature engineering in a flight information system is described in this paper. We used feature engineering to improve the performance of machine learning classifiers for predicting flight delays and describe various techniques for extracting and constructing features from the raw data, including time-based features, trend-based features, and error-based features. Before applying these techniques, we applied feature pre-processing techniques, including the CTAO algorithm for feature pre-processing, followed by the SCSO (Sand cat swarm optimization) algorithm for feature extraction and the Enhanced harmony search for feature optimization. The resultant feature set contained the 9 most relevant features for deciding whether a flight would be delayed or not. Additionally, we evaluate the performance of various classifiers using these engineered features and contrast the results with those obtained using raw features. The results show that feature engineering significantly improves the performance of the classifiers and allows for more accurate prediction of flight delays in real-time.

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Dube, R. P., and H. R. Johnson. "Computer-Assisted Engineering Data Base." Journal of Engineering for Industry 107, no. 1 (February 1, 1985): 33–38. http://dx.doi.org/10.1115/1.3185961.

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General capabilities of data base management technology are described. Information requirements posed by the space station life cycle are discussed, and it is asserted that data base management technology supporting engineering/manufacturing in a heterogeneous hardware/data base management system environment should be applied to meeting these requirements. Today’s commercial systems do not satisfy all of these requirements. The features of an R&D data base management system being developed to investigate data base management in the engineering/manufacturing environment are discussed. Features of this system represent only a partial solution to space station requirements. Areas where this system should be extended to meet full space station information management requirements are discussed.

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Shrestha, Sushil, and Manish Pokharel. "Educational data mining in moodle data." International Journal of Informatics and Communication Technology (IJ-ICT) 10, no. 1 (April 1, 2021): 9. http://dx.doi.org/10.11591/ijict.v10i1.pp9-18.

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<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click, and Wiki.Click was used as the independent features and Grade as the dependent feature. Five classification algorithms such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine (SVM), Random Forest, and CART decision tree were applied in the moodle data. The finding shows that SVM has the highest accuracy in comparison to other algorithms. It suggested that File.Click and System.Click was the most significant feature. This type of research helps in the early identification of students’ performance. The growing popularity of the teaching-learning process through an online learning system has attracted researchers to work in the field of Educational Data Mining (EDM). Varieties of data are generated through several online activities that can be analyzed to understand the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platforms for the delivery of the course contents and the learners who use these platforms are highly benefited from this research.</p>

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Zhang, Song. "The Construction of Modern Administrative Law via Data Mining." Archives des Sciences 74, s1 (August 10, 2024): 32–39. http://dx.doi.org/10.62227/as/74s16.

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Early administrative jurisprudence generally experienced a shift from administrative science to legal science, while modern administrative jurisprudence has shifted from judicial review to administrative process centered. In this paper, we introduce feature engineering technology into the construction model of administrative law based on data mining. The prior knowledge is introduced into the model through the artificially constructed effective features, and the neural network method can automatically extract features such as abstract features from the original input text. We propose a deep feature engineering method to combine the advantages of both in the study of relation extraction tasks. Experiments show that our method provides a powerful legal analysis tool, which helps to innovate the conceptual framework and theoretical system of traditional administrative law and establish a modern administrative law system that can explain the real-world administrative process.

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Huang, Eunchong, Sarah Kim, and TaeJin Ahn. "Deep Learning for Integrated Analysis of Insulin Resistance with Multi-Omics Data." Journal of Personalized Medicine 11, no. 2 (February 15, 2021): 128. http://dx.doi.org/10.3390/jpm11020128.

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Technological advances in next-generation sequencing (NGS) have made it possible to uncover extensive and dynamic alterations in diverse molecular components and biological pathways across healthy and diseased conditions. Large amounts of multi-omics data originating from emerging NGS experiments require feature engineering, which is a crucial step in the process of predictive modeling. The underlying relationship among multi-omics features in terms of insulin resistance is not well understood. In this study, using the multi-omics data of type II diabetes from the Integrative Human Microbiome Project, from 10,783 features, we conducted a data analytic approach to elucidate the relationship between insulin resistance and multi-omics features, including microbiome data. To better explain the impact of microbiome features on insulin classification, we used a developed deep neural network interpretation algorithm for each microbiome feature’s contribution to the discriminative model output in the samples.

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Li, Songyuan, Yuyan Man, Chi Zhang, Qiong Fang, Suya Li, and Min Deng. "PRPD data analysis with Auto-Encoder Network." E3S Web of Conferences 81 (2019): 01019. http://dx.doi.org/10.1051/e3sconf/20198101019.

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Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge.

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Li, Zongze. "Feature Engineering and Data Visualization Analysis in Artificial Intelligence in Big Data Era." International Journal of Computer Science and Information Technology 3, no. 3 (August 12, 2024): 390–95. http://dx.doi.org/10.62051/ijcsit.v3n3.41.

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In the environment of massive data, the selection and construction of feature engineering plays a crucial role in the performance and accuracy of sgon models. It is true that the classic hand-driven feature building method can incorporate insights from the professional field, but this method is potentially accompanied by the hidden trouble of information omission, and does not necessarily touch the boundary of the optimal solution. In order to solve these problems, this paper proposes two strategies of feature extraction: ensemble learning and deep learning. Ensemble learning enhances generalization by combining the opinions of multiple models, while deep learning allows models to automatically learn features, reducing the need for human intervention. Both of these methods can overcome the limitations of manual feature design to varying degrees. In addition, the paper also introduces the application of parallel coordinate graph in feature selection. By using the parallel axis system to implement projection transformation of high-dimensional data, scholars can intuitively analyze the data structure, so as to promote the process of feature selection and optimization. This method not only gives insight into the subtle relationship between the data, but also cleverly stimulates the potential of human pattern recognition and further improves the comprehensive performance of the model.

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Lu, Songyuanyi. "Technical Features and Trends of Data Science in Financial Engineering." Frontiers in Business, Economics and Management 4, no. 3 (July 31, 2022): 34–37. http://dx.doi.org/10.54097/fbem.v4i3.1068.

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In the new financial era, the huge amount of data brings more challenges to the traditional financial business and creates unprecedented opportunities at the same time. In the financial industry, the use of data science by financial institutions has significantly deepened, from the traditional "data visualization presentation" to "data-based decision analysis". This paper analyzes the technical characteristics and development trend of data science in financial engineering against the background of rapid development of financial technology.

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Chen, Jingcheng, Yining Sun, and Shaoming Sun. "Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering." Sensors 21, no. 3 (January 20, 2021): 692. http://dx.doi.org/10.3390/s21030692.

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Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

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Salii, Yevhenii, Alla Lavreniuk, and Nataliia Kussul. "Statistical methods of feature engineering for the problem of forest state classification using satellite data." System research and information technologies, no. 1 (March 29, 2024): 86–98. http://dx.doi.org/10.20535/srit.2308-8893.2024.1.07.

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Timely detection of forest diseases is an important task for their prevention and spread limitation. The usage of satellite imagery provides capabilities for large-scale forest monitoring. Machine learning models allow to automate the analysis of these data for anomaly detection indicating diseases. However, selecting informative features is key to building an effective model. In this work, the application of Bhattacharyya distance and Spearman’s rank correlation coefficient for feature selection from satellite images was investigated. A greedy algorithm was applied to form a subset of weakly correlated features. The experiment showed that selected features allow for improving the classification quality compared to using all spectral bands. The proposed approach demonstrates effectiveness for informative and weakly correlated feature selection and can be utilized in other remote sensing tasks.

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Dissertations / Theses on the topic "Data / features engineering"

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Mohammed, Hussein Syed. "Random feature subspace ensemble based approaches for the analysis of data with missing features /." Full text available online, 2006. http://www.lib.rowan.edu/find/theses.

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Baik, Edward H. (Edward Hyeen). "Surface-based segmentation of volume data using texture features." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43516.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (p. 117-123).
by Edward H. Baik.
M.Eng.

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Campbell, Richard John. "Recognition of free-form 3D objects in range data using global and local features /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486397841221694.

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Oldfield, Robin B. "Lithological mapping of Northwest Argentina with remote sensing data using tonal, textural and contextual features." Thesis, Aston University, 1988. http://publications.aston.ac.uk/14287/.

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Tonal, textural and contextual properties are used in manual photointerpretation of remotely sensed data. This study has used these three attributes to produce a lithological map of semi arid northwest Argentina by semi automatic computer classification procedures of remotely sensed data. Three different types of satellite data were investigated, these were LANDSAT MSS, TM and SIR-A imagery. Supervised classification procedures using tonal features only produced poor classification results. LANDSAT MSS produced classification accuracies in the range of 40 to 60%, while accuracies of 50 to 70% were achieved using LANDSAT TM data. The addition of SIR-A data produced increases in the classification accuracy. The increased classification accuracy of TM over the MSS is because of the better discrimination of geological materials afforded by the middle infra red bands of the TM sensor. The maximum likelihood classifier consistently produced classification accuracies 10 to 15% higher than either the minimum distance to means or decision tree classifier, this improved accuracy was obtained at the cost of greatly increased processing time. A new type of classifier the spectral shape classifier, which is computationally as fast as a minimum distance to means classifier is described. However, the results for this classifier were disappointing, being lower in most cases than the minimum distance or decision tree procedures. The classification results using only tonal features were felt to be unacceptably poor, therefore textural attributes were investigated. Texture is an important attribute used by photogeologists to discriminate lithology. In the case of TM data, texture measures were found to increase the classification accuracy by up to 15%. However, in the case of the LANDSAT MSS data the use of texture measures did not provide any significant increase in the accuracy of classification. For TM data, it was found that second order texture, especially the SGLDM based measures, produced highest classification accuracy. Contextual post processing was found to increase classification accuracy and improve the visual appearance of classified output by removing isolated misclassified pixels which tend to clutter classified images. Simple contextual features, such as mode filters were found to out perform more complex features such as gravitational filter or minimal area replacement methods. Generally the larger the size of the filter, the greater the increase in the accuracy. Production rules were used to build a knowledge based system which used tonal and textural features to identify sedimentary lithologies in each of the two test sites. The knowledge based system was able to identify six out of ten lithologies correctly.

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Mora, Omar Ernesto. "Morphology-Based Identification of Surface Features to Support Landslide Hazard Detection Using Airborne LiDAR Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429861576.

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Fridley, Lila (Lila J. ). "Improving online demand forecast using novel features in website data : a case study at Zara." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117976.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 77).
The challenge of improving retail inventory customer service level while reducing costs is common across many retailers. This problem is typically addressed through efficient supply chain operations. This thesis discusses the development of new methodologies to predict e-commerce consumer demand for seasonal, short life-cycle articles. The new methodology incorporates novel data to predict demand of existing products through a bottom-up point forecast at the color and location level. It addresses the widely observed challenge of forecasting censored demand during a stock out. Zara introduces thousands of new items each season across over 2100 stores in 93 markets worldwide [1]. The Zara Distribution team is responsible for allocating inventory to each physical and e-commerce store. In line with Zara's quick to retail strategy, Distribution is flexible and responsive in forecasting store demand, with new styles arriving in stores twice per week [1]. The company is interested in improving the demand forecast by leveraging the novel e-commerce data that has become available since the launch of Zara.com in 2010 [2]. The results of this thesis demonstrate that the addition of new data to a linear regression model reduces prediction error by an average of 16% for e-commerce articles experiencing censored demand during a stock out, in comparison to traditional methods. Expanding the scope to all e-commerce articles, this thesis demonstrates that incorporating easily accessible web data yields an additional 2% error reduction on average for all articles on a color and location basis. Traditional methods to improve demand prediction have not before leveraged the expansive availability of e-commerce data, and this research presents a novel solution to the fashion forecasting challenge. This thesis project may additionally be used as a case-study for companies using subscriptions or an analogous tracking tool, as well as novel data features, in a user-friendly and implementable demand forecast model.
by Lila Fridley.
M.B.A.
S.M.

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Wang, Ziang. "People Matching for Transportation Planning Using Optimized Features and Texel Camera Data for Sequential Estimation." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1298.

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This thesis explores pattern recognition in the dynamic setting of public transportation, such as a bus, as people enter and later exit from a doorway. Matching the entrance and exit of each individual provides accurate information about individual riders such as how long a person is on a bus and which stops the person uses. At a higher level, matching exits to entries provides information about the distribution of traffic flow across the whole transportation system. A texel camera is implemented and multiple measures of people are made where the depth and color data are generated. A large number of features are generated and the sequential floating forward selection (SFFS) algorithm is used for selecting the optimized features. Criterion functions using marginal accuracy and maximization of minimum normalized Mahalanobis distance are designed and compared. Because of the particular case of the bus environment, which is a sequential estimation problem, a trellis optimization algorithm is designed based on a sequence of measurements from the texel camera. Since the number of states in the trellis grows exponentially with the number of people currently on the bus, a beam search pruning technique is employed to manage the computational and memory load. Experimental results using real texel camera measurements show good results for 68 people exiting from an initially full bus in a randomized order. In a bus route simulation where a true traffic flow distribution is used to randomly draw entry and exit events for simulated riders, the proposed sequential estimation algorithm produces an estimated traffic flow distribution which provides an excellent match to the true distribution.

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Katzwinkel, Tim, Bhavinbhai Patel, Alexander Schmid, Walter Schmidt, Justus Siebrecht, Manuel Löwer, and Jörg Feldhusen. "Kosteneffiziente Technologien zur geometrischen Datenaufnahme im digitalen Reverse Engineering." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-215118.

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Zusammenfassung Der vorliegende Beitrag schlägt eine Auswahlmethode vor, die geeignete Verfahren zur kosteneffizienten Rekonstruktion geometrischer Daten von Baugruppen und Bauteilen aufzeigt. Dabei werden verschiedene objektbezogene Einflussfaktoren wie beispielsweise die Bauteilkomplexität, vorhandene Standardfeatures (z.B. genormte Gewindebohrungen) oder besondere Oberflächengeometrien berücksichtigt. Darüber hinaus werden verschiedene Techniken anhand der Kriterien zeitlicher Aufwand, technologischer Aufwands und erzielbarer Maßgenauigkeit quantitativ verglichen. Dadurch kann der Anwender einen erforderlichen Kompromiss zwischen kostenmäßigem Aufwand und erzielbarer Maßgenauigkeit abschätzen.

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Fabijan, Aleksander. "Developing the right features : the role and impact of customer and product data in software product development." Licentiate thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-7794.

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Software product development companies are increasingly striving to become data-driven. The access to customer feedback and product data has been, with products increasingly becoming connected to the Internet, demonetized. Systematically collecting the feedback and efficiently using it in product development, however, are challenges that large-scale software development companies face today when being faced by large amounts of available data. In this thesis, we explore the collection, use and impact of customer feedback on software product development. We base our work on a 2-year longitudinal multiple-case study research with case companies in the software-intensive domain, and complement it with a systematic review of the literature. In our work, we identify and confirm that large-software companies today collect vast amounts of feedback data, however, struggle to effectively use it. And due to this situation, there is a risk of prioritizing the development of features that may not deliver value to customers. Our contribution to this problem is threefold. First, we present a comprehensive and systematic review of activities and techniques used to collect customer feedback and product data in software product development. Next, we show that the impact of customer feedback evolves over time, but due to the lack of sharing of the collected data, companies do not fully benefit from this feedback. Finally, we provide an improvement framework for practitioners and researchers to use the collected feedback data in order to differentiate between different feature types and to model feature value during the lifecycle. With our contributions, we aim to bring software companies one step closer to data-driven decision making in software product development.

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Erdogan, Ozgur. "Main Seismological Features Of Recently Compiled Turkish Strong Motion Database." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609679/index.pdf.

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In this thesis it is aimed to compile the Turkish strong-motion database for its efficient use in earthquake engineering and strong-motion seismology related studies. Within this context, the Turkish strong-motion database is homogenized in terms of basic earthquake source parameters (e.g. magnitude, style-of-faulting) as well as site classes and different source-to-site distance metrics. As part of this objective, empirical relationships for different magnitude scales are presented for further harmonization of the database. Data processing of the selected raw (unprocessed) strong-motion accelerograms that do not suffer from non-standard problems are realized. A comparative study is also conducted between the peak ground-motion values of Turkish strong-motion database with the estimations computed from different ground-motion prediction models. This way the regional differences of Turkish database are evaluated by making use of global prediction models. It is believed that the main products of this thesis will be of great use for reliable national seismic risk and hazard studies.

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Books on the topic "Data / features engineering"

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Safdar, Mutahar, Guy Lamouche, Padma Polash Paul, Gentry Wood, and Yaoyao Fiona Zhao. Engineering of Additive Manufacturing Features for Data-Driven Solutions. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32154-2.

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H, Fong Henry, and PDA Engineering (Firm : Santa Ana, Calif.). Software Products Division., eds. PATRAN II: A guide to new features and enhancements. Santa Ana, Calif. (1560 Brookhollow Dr., Santa Ana 92705-5475): PDA Engineering, Software Products Division, 1985.

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Bartashevich, Aleksandr, Vladimir Onegin, Sergey Trofimov, and Sergey Gayduk. Design furniture. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1025973.

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Reviewed all types of furniture and the basic requirements to it, the history of its development, structural and style features modern furniture, basics of design engineering design. A General characteristic properties of production materials and normative-reference data necessary for the development of interiors and furniture. In detail the questions of automation of designing of furniture. Considered furniture in the interior and analyzed all aspects of engineering products for various purposes for premises of residential and public buildings. For students of higher educational institutions trained on specialities "Technology of logging and wood processing industries" and "Design". Will be useful to students of colleges, higher and secondary vocational and technical schools woodworking profile. Can serve as a reference book for architects, designers, and all production workers in the furniture industry.

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Zuev, Sergey, Daut Yahutl', Boris Bass, and Ruslan Maleev. Ignition devices for fuel-air mixture of heat engines. ru: INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/1911604.

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The textbook describes the basic theoretical foundations and practical tasks in the field of research of ignition devices of fuel-air mixture of heat engines. The data concerning the working conditions of spark plugs, their classification, device and main characteristics are presented. The features of electrophysical processes in spark plugs of automotive internal combustion engines are described in detail. The methodology and algorithms of numerical simulation of the thermal state of a spark plug are considered. The development, testing and quality control, production and operation of ignition devices for fuel-air mixture of heat engines are described. Meets the requirements of the latest generation of federal state standards of higher education. It is intended for undergraduate, graduate and specialist students studying in the fields of 13.03.02 "Electric Power engineering and electrical engineering", 12.03.01 "Instrument Engineering", 12.03.05 "Laser technology and laser technologies", 12.05.01 "Electronic and optoelectronic devices and special purpose systems".

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Ozdemir, Sinan, and Divya Susarla. Feature Engineering Made Easy: Identify unique features from your dataset in order to build powerful machine learning systems. Packt Publishing, 2018.

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Modeling Hydrologic Effects Of Microtopographic Features. Nova Science Publishers, 2011.

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(Editor), Ian Flood, and Nabil Kartam (Editor), eds. Artificial Neural Networks for Civil Engineers: Advanced Features and Applications. American Society of Civil Engineers, 1998.

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Safdar, Mutahar, Guy Lamouche, and Gentry Wood. Engineering of Additive Manufacturing Features for Data-Driven Solutions: Sources, Techniques, Pipelines, and Applications. Springer, 2023.

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Baran, Nicholas M. R:Base System 5 Including R:Base 5000; The Microsoft Reference Guide to All Commands, Functions and Features (Command Performance Series). Microsoft Press, 1987.

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Feature Engineering for Machine Learning and Data Analytics. Taylor & Francis Group, 2018.

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Book chapters on the topic "Data / features engineering"

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Taniguchi, Masanobu, Tomoyuki Amano, Hiroaki Ogata, and Hiroyuki Taniai. "Features of Financial Data." In Statistical Inference for Financial Engineering, 1–39. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03497-3_1.

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Del Frate, Fabio, and Matteo Picchiani. "Features Extraction from Satellite Data." In Encyclopedia of Earthquake Engineering, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-36197-5_223-1.

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Del Frate, Fabio, and Matteo Picchiani. "Features Extraction from Satellite Data." In Encyclopedia of Earthquake Engineering, 1039–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35344-4_223.

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Dai, Lingna, Fei Gao, Rongsheng Li, Jiachen Yu, Xiaoyuan Shen, Huilin Xiong, and Weilun Wu. "Gated Fusion of Discriminant Features for Caricature Recognition." In Intelligence Science and Big Data Engineering. Visual Data Engineering, 563–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36189-1_47.

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Sangha, Ratinder Kaur, and Preeti Rai. "An Appearance-Based Gender Classification Using Radon Features." In Data, Engineering and Applications, 159–69. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6347-4_15.

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Safdar, Mutahar, Guy Lamouche, Padma Polash Paul, Gentry Wood, and Yaoyao Fiona Zhao. "Feature Engineering in Additive Manufacturing." In Engineering of Additive Manufacturing Features for Data-Driven Solutions, 17–43. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32154-2_2.

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Chang, Yuan-chin Ivan, Haoran Hsu, and Lin-Yi Chou. "Graphical Features Selection Method." In Intelligent Data Engineering and Automated Learning — IDEAL 2002, 475–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45675-9_71.

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Han, Yiqiu, and Wai Lam. "Exploiting Heterogeneous Features for Classification Learning." In Intelligent Data Engineering and Automated Learning, 177–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_25.

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Yangui, Rania, Ahlem Nabli, and Faiez Gargouri. "SOIM: Similarity Measures on Ontology Instances Based on Mixed Features." In Model and Data Engineering, 169–76. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11587-0_17.

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Singh, Manan, and Kavi Narayana Murthy. "Authorship Attribution using Filtered N-grams as Features." In Data Engineering and Communication Technology, 379–90. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0081-4_38.

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Conference papers on the topic "Data / features engineering"

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Yuan, Run, and Haonan Long. "Driver fatigue detection based on multi-feature fusion facial features." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 683–86. IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762609.

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Hibatullah, Muhammad Helmi, and Yani Widyani. "SRS for Software with Machine Learning Features." In 2024 IEEE International Conference on Data and Software Engineering (ICoDSE), 211–16. IEEE, 2024. https://doi.org/10.1109/icodse63307.2024.10829896.

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Ye, Dandan. "Reversible Data Hiding for Clustering Based on Pixel Texture Features." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE), 343–47. IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824492.

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Joshi, Nilesh S., and Jami J. Shah. "On the Viability of Developing CAD Data Exchange Standard for Form Features." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85606.

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Form feature data exchange is divided into three types: CAD-to-CAD, CAD-to-Downstream applications and inter-downstream applications. Essential characteristics for CAD-to-CAD and CAD-to-Downstream types of feature data transfer are established flowed by a set of criteria for evaluation of a form feature exchange schema. Contemporary neutral feature data exchange schemas like AP 224, AP 203 and NRep are evaluated. It is concluded that none of them is fully equipped to do the job. AP 203 belongs to the CAD-to-CAD feature data exchange class. It exchanges only the final part geometry and the feature model is lost. AP 224 and NRep belong to the CAD-to-Downstream class. AP 224 attempts to enlist all features that can be manufactured using milling and turning processes. It limits the user to finite set of features. On the other hand, NRep permits the user to define his own features and does not provide a standard set. For a complete feature data transfer between two CAD applications, one needs to model the design intent of each design feature and transfer it with construction history of creation of the part model while for an efficient feature data transfer between CAD and downstream applications, the schema needs to standardize a set of most common features but also provide means for the user to define customized features with desired parameterization and attributes.

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Mirza, Waqas, and Irfan Manarvi. "Laptop selection using data mining of critical features." In Industrial Engineering (CIE39). IEEE, 2009. http://dx.doi.org/10.1109/iccie.2009.5223722.

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Du, Ping, and Erin F. MacDonald. "Eye-Tracking Data Predicts Importance of Product Features and Saliency of Size Change." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12737.

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Features, or visible product attributes, are indispensable product components that influence customer evaluations of functionality, usability, symbolic impressions and other qualities. Two basic components of features are visual appearance and size. This work tests whether or not eye-tracking data can (1) predict the relative importances between features, with respect to their visual design, in overall customer preference; and (2) identify how much a feature must change in size in order to be noticeable by the viewer. The results demonstrate that feature importance is significantly correlated with a variety of gaze data. Results also show that there are significant differences in fixation time and count for noticeable vs. unnoticeable size changes. Logistic models of gaze data can predict both feature importance and saliency of size change.

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Stanik, Christoph, Marlo Haering, Chakajkla Jesdabodi, and Walid Maalej. "Which App Features Are Being Used? Learning App Feature Usages from Interaction Data." In 2020 IEEE 28th International Requirements Engineering Conference (RE). IEEE, 2020. http://dx.doi.org/10.1109/re48521.2020.00019.

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Zeng, Weixin, Xiang Zhao, Jiuyang Tang, and Xuemin Lin. "Collective Entity Alignment via Adaptive Features." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00191.

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Hanayneh, Leen, Yiwen Wang, Yan Wang, Jack C. Wileden, and Khurshid A. Qureshi. "Feature Mapping Automation for CAD Data Exchange." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49671.

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Computer-aided design (CAD) data interoperability is one of the most important issues to enable information integration and sharing in a collaborative engineering environment. A significant amount of work has been done on the extension and standardization of neutral data formats in both academy and industry. In this paper, we present a feature mapping mechanism to allow for automatic feature information exchange. A hybrid semantic feature model is used to represent implicit and explicit features. A graph-based feature isomorphism algorithm is developed to support feature mapping between different CAD data formats.

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Spathis, Prométhée, and Raul Adrian Gorcitz. "A data-driven analysis of YouTube community features." In the 7th Asian Internet Engineering Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2089016.2089019.

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Reports on the topic "Data / features engineering"

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Rana, Arnav, and Sanjay Tiku. PR-214-223806-R01 Guidance for Performing Engineering Critical Assessments for Dents on Natural Gas Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 2023. http://dx.doi.org/10.55274/r0000044.

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This project builds on mechanical damage (MD) assessment and management tools, developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), American Petroleum Institute (API), other research organizations and individual pipeline operators and included in API RP 1183 [1]. These include dent shape, restraint condition and interacting feature characterization; operational maximum and cyclic internal pressure characterization, screening tools defining non-injurious dent shapes based on pipe size and operating condition, failure pressure and fatigue assessment tools for dents with/without interacting features (e.g., corrosion, welds, gouges) in the restrained and unrestrained condition, and direction on available remedial action and repair techniques. The API RP 1183 [1], has not been adopted by the Pipeline and Hazardous Materials Safety Administration (PHMSA) by reference in code of federal regulations (CFR) 192.712 (c). CFR 192.712 (c) allows pipeline operators to follow certain prescriptive requirements for responding to mechanical damage features or perform an engineering critical assessment (ECA). The requirements of CFR 192.712 (c) provide minimum requirements for what would comprise an acceptable ECA. The objective of this research project is to develop a guidance document containing a practical and defensible set of guidelines and processes to address the CFR 192.712 (c) requirements. The work included: - Description of various dent fatigue life screening and assessment approaches detailing data requirements for the different approaches, - Developing a simplified method for dent fatigue life assessment using operational severity when detailed pressure spectrum data is not available, - Development of a Level 0.75 and 0.75+ screening approach that incorporates dent depth available from in-line inspection (ILI) data, - Developing a screening level methodology to carry out fatigue life assessment of dents with potential gouge where metal loss is conservatively assumed to be a planar crack-like feature.

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Tiku, Sanjay, Arnav Rana, Binoy John, and Aaron Dinovitzer. PR-214-203805-R01 Performance Evaluation of ILI Systems for Dents and Coincident Features. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2024. http://dx.doi.org/10.55274/r0000056.

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Pipeline integrity management involves the analysis of pipeline condition information (e.g., pipe size, presence and size of features), operational/environmental conditions and line pipe material properties in engineering assessment (fitness-for-purpose) tools to evaluate operational risk. While nominal or minimum specified material properties and SCADA reported, design or estimated operational loading conditions can be considered, pipeline operators depend heavily on pipeline condition data from in-line inspection (ILI) systems. The current project presents the details of performance trials evaluating the ability of ILI systems to provide pipeline condition information for dents with coincident or closely aligned features. A set of sample dent features were prepared along with a trial protocol and performance metrics beyond those presented in API 1163 that were used to characterize performance. ILI system pull and pump through trials of magnetic, ultrasonic and caliper-based ILI technologies from four ILI Service Providers were performed. Data from these trials were used to quantify detection, identification, and sizing performance of the ILI systems for isolated corrosion features, dents with variety of shapes including those without coincident features and those with corrosion, gouges and/or cracks. The effect of dents on the ILI system detection, identification and sizing of the coincident features was evaluated.

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Tiku, Sanjay. PR-214-203820-R01 Performance Evaluation of ILI for Dents with Cracks and Gouges. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), July 2023. http://dx.doi.org/10.55274/r0000031.

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Pipeline integrity management involves the analysis of pipeline condition information (e.g., pipe size, presence and size of features), operational/environmental conditions and line pipe material properties in engineering assessment (fitness-for-purpose) tools to evaluate operational risk. While nominal or minimum specified material properties and SCADA reported, design or estimated operational loading conditions can be considered, pipeline operators depend heavily on pipeline condition data from in-line inspection (ILI) systems. The current project presents the details of performance trials evaluating the ability of ILI systems to provide pipeline condition information for dents with coincident or closely aligned features. A set of sample dent features were prepared along with a trial protocol and performance metrics beyond those presented in API 1163 that were used to characterize performance. ILI system pull and pump through trials of magnetic, ultrasonic and caliper-based ILI technologies from seven ILI Service Providers were performed. Data from these trials were used to quantify detection, identification, and sizing performance of the ILI systems for isolated corrosion features, dents with variety of shapes including those without coincident features and those with corrosion, gouges and/or cracks. The effect of dents on the ILI system detection, identification and sizing of the coincident features was evaluated.

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Berkowitz, Jacob, Nathan Beane, Kevin Philley, Nia Hurst, and Jacob Jung. An assessment of long-term, multipurpose ecosystem functions and engineering benefits derived from historical dredged sediment beneficial use projects. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41382.

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The beneficial use of dredged materials improves environmental outcomes while maximizing navigation benefits and minimizing costs, in accordance with the principles of the Engineering With Nature® (EWN) initiative. Yet, few studies document the long-term benefits of innovative dredged material management strategies or conduct comprehensive life-cycle analysis because of a combination of (1) short monitoring time frames and (2) the paucity of constructed projects that have reached ecological maturity. In response, we conducted an ecological functional and engineering benefit assessment of six historic (>40 years old) dredged material–supported habitat improvement projects where initial postconstruction beneficial use monitoring data was available. Conditions at natural reference locations were also documented to facilitate a comparison between natural and engineered landscape features. Results indicate the projects examined provide valuable habitat for a variety of species in addition to yielding a number of engineering (for example, shoreline protection) and other (for example, carbon storage) benefits. Our findings also suggest establishment of ecological success criteria should not overemphasize replicating reference conditions but remain focused on achieving specific ecological functions (that is, habitat and biogeochemical cycling) and engineering benefits (that is, storm surge reduction, navigation channel maintenance) achievable through project design and operational management.

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Geisthardt, Eric, Burton Suedel, and John Janssen. Monitoring the Milwaukee Harbor breakwater : an Engineering With Nature® (EWN®) demonstration project. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/40022.

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The US Army Corps of Engineers (USACE) maintains breakwaters in Milwaukee Harbor. USACE’s Engineering With Nature® (EWN®) breakwater demonstration project created rocky aquatic habitat with cobbles (10–20 cm) covering boulders (6–8 metric tons) along a 152 m section. A prolific population of Hemimysis anomala, an introduced Pontocaspian mysid and important food source for local pelagic fishes, was significantly (p < .05) more abundant on cobbles versus boulders. Food-habits data of alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax) provided evidence that H. anomala were a common prey item. Night surveys and gill netting confirmed O. mordax preferred foraging on the cobbles (p < .05) and consumed more H. anomala than at the reference site (p < .05). H. anomala comprised a significant portion of the diets of young-of-the-year (YOY) yellow perch (Perca flavescens), YOY largemouth bass (Micropterus salmoides), and juvenile rock bass (Ambloplites rupestris) caught on the breakwater. The natural features’ construction on the breakwater increased the available habitat for this benthopelagic macroinvertebrate and created a novel ecosystem benefiting forage fish and a nursery habitat benefiting nearshore game fish juveniles. These data will encourage the application of EWN concepts during structural repairs at other built navigation infrastructure.

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Tiku, Sanjay, Arnav Rana, and Binoy John. PR214-213800-R01 Evaluation of API RP 1183 Dent Fatigue Analyses using In-Service Dents Data. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), October 2024. http://dx.doi.org/10.55274/r0000092.

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The objective of this project is to validate the existing mechanical damage (MD) assessment and management tools based on in-service field dents data provided by pipeline operators. The data provided included in-line inspection (ILI), dent geometry data, operating pressure history data and in-ditch inspection observations. The dent fatigue life analysis tools were developed on behalf of Pipeline Research Council International (PRCI), Interstate Natural Gas Association of America (INGAA), Canadian Energy Pipeline Association (CEPA), other research organizations and individual pipeline operators and are included in API Recommended Practice (RP) 1183 (1). Since the assembly of API RP 1183, PRCI has continued its mechanical damage strategic research priority in the development of a greater understanding of the behavior of mechanical damage and the production of data to support engineering assessment. The research work included the following series of tasks: - Collect and collate mechanical damage field data provided by various pipeline operators. - Implement the fatigue life screening and assessment approaches using the provided dent geometry and pressure loading data. - Validate the fatigue life assessment results against the in-ditch inspection data. The data included presence of through wall cracks in dents resulting in leaks, location of surface cracks within dents and co-incident features like welds, corrosion, or gouges.

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, including the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.

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Ingensand, Jens, and Kalimar Maia, eds. OGC Vector Tiles Pilot: Tiled Feature Data Conceptual Model Engineering Report. Open Geospatial Consortium, Inc., February 2019. http://dx.doi.org/10.62973/18-076.

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Whitfield, Paula, Jenny Davis, Amanda Tritinger, Danielle Szimanski, Rebecca Golden, Joseph Gailani, Michael Ramirez, Brook Herman, Matt Whitbeck, and Jeffery King. Swan Island : monitoring and adaptive management plan. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45044.

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Swan Island is a 10.12 ha island located in the Maryland waters of the Chesapeake Bay. Because of its value as a natural wave break for the town of Ewell on nearby Smith Island, as well as the ongoing erosion and subsidence of the island, in 2019 US Army Corps of Engineers (USACE)–Baltimore District placed 45,873 m³ of dredged sediment and planted 200,000 marsh plants. This restoration provided an opportunity to quantify the engineering (that is, resilience) and ecological performance of the island, postplacement. The lack of quantitative data on the performance of natural features such as islands has led to perceived uncertainties that are often cited as barriers to implementation. To address these data gaps, a multidisciplinary collaboration of five government entities identified project objectives and monitoring parameters through a series of mediated workshops and then developed a conceptual model to articulate those parameters and the linkages between them. This monitoring and adaptive management plan (MAMP) documents those monitoring parameters and procedures and can serve as an example for other scales, regions, and research questions. Documenting research and monitoring efforts may help to foster widespread acceptance of nature-based solutions such as islands.

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Eastman, Brittany. Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy Rights. SAE International, July 2022. http://dx.doi.org/10.4271/epr2022016.

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Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and addresses some of the legitimate concerns regarding its use by private corporations and government entities, including law enforcement, in public transportation hubs and traffic stops. Constitutional questions, including First, Forth, and Ninth Amendment issues, also remain unanswered. FRS is now a permanent part of transportation technology and society; with meaningful legislation and conscious engineering, it can make future transportation safer and more convenient.

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