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Relevant bibliographies by topics / Data correlation with time stamp

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Journal articles

Academic literature on the topic 'Data correlation with time stamp'

Author: Grafiati

Published: 10 March 2023

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Journal articles on the topic "Data correlation with time stamp"

1

Jensen, Terry, Roy Brown, Gay Riegel, Lalan S. Wilfong, and John Russell Hoverman. "Time stamps used to measure the patient's clinic experience." Journal of Clinical Oncology 34, no. 7_suppl (2016): 150. http://dx.doi.org/10.1200/jco.2016.34.7_suppl.150.

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150 Background: In 2013, a patient reported satisfaction survey indicated 19% of patients waited 20-40 minutes, 8% 40-60 minutes and 4% over 1 hour. We initiated a project to objectively quantify the components of wait times to investigate opportunities for improvement. Methods: Utilizing existing technology in the practice management system, clinic staff use the Day List feature to capture time stamps as patients move through the clinic. We focused on provider appointments but these visits could also include business office, labs, infusion and diagnostics. It was important to define where the

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2

Fisher-Levine, Merlin, Rebecca Boll, Farzaneh Ziaee, et al. "Time-resolved ion imaging at free-electron lasers using TimepixCam." Journal of Synchrotron Radiation 25, no. 2 (2018): 336–45. http://dx.doi.org/10.1107/s1600577517018306.

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The application of a novel fast optical-imaging camera, TimepixCam, to molecular photoionization experiments using the velocity-map imaging technique at a free-electron laser is described. TimepixCam is a 256 × 256 pixel CMOS camera that is able to detect and time-stamp ion hits with 20 ns timing resolution, thus making it possible to record ion momentum images for all fragment ions simultaneously and avoiding the need to gate the detector on a single fragment. This allows the recording of significantly more data within a given amount of beam time and is particularly useful for pump–probe expe

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Guo, Canyang, Genggeng Liu, and Chi-Hua Chen. "Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network." Wireless Communications and Mobile Computing 2020 (October 5, 2020): 1–13. http://dx.doi.org/10.1155/2020/8854649.

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The global environment has become more polluted due to the rapid development of industrial technology. However, the existing machine learning prediction methods of air quality fail to analyze the reasons for the change of air pollution concentration because most of the prediction methods take more focus on the model selection. Since the framework of recent deep learning is very flexible, the model may be deep and complex in order to fit the dataset. Therefore, overfitting problems may exist in a single deep neural network model when the number of weights in the deep neural network model is lar

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Khairul Anuar, Noor Hafizah, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim, Shafishuhaza Sahlan, and Mahdi Faramarzi. "An assessment of stingless beehive climate impact using multivariate recurrent neural networks." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2030. http://dx.doi.org/10.11591/ijece.v13i2.pp2030-2039.

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<p>A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different for

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Meyerson, William U., Sarah K. Fineberg, Ye Kyung Song, et al. "Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys." JMIR Formative Research 7 (January 17, 2023): e38112. http://dx.doi.org/10.2196/38112.

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Background Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community. Objecti

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Sheu, Ruey-Kai, Mayuresh Pardeshi, Lun-Chi Chen, and Shyan-Ming Yuan. "STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters." Sensors 19, no. 13 (2019): 3016. http://dx.doi.org/10.3390/s19133016.

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There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be

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McAlister, Merritt. ""Downright Indifference": Examining Unpublished Decisions in the Federal Courts of Appeals." Michigan Law Review, no. 118.4 (2020): 533. http://dx.doi.org/10.36644/mlr.118.4.downright.

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Nearly 90 percent of the work of the federal courts of appeals looks nothing like the opinions law students read in casebooks. Over the last fifty years, the so-called “unpublished decision” has overtaken the federal appellate courts in response to a caseload volume “crisis.” These are often short, perfunctory decisions that make no law; they are, one federal judge said, “not safe for human consumption.” The creation of the inferior unpublished decision also has created an inferior track of appellate justice for a class of appellants: indigent litigants. The federal appellate courts routinely

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Davies, Alyse, Margaret Allman-Farinelli, Katherine Owen, et al. "Feasibility Study Comparing Physical Activity Classifications from Accelerometers with Wearable Camera Data." International Journal of Environmental Research and Public Health 17, no. 24 (2020): 9323. http://dx.doi.org/10.3390/ijerph17249323.

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Device-based assessments are frequently used to measure physical activity (PA) but contextual measures are often lacking. There is a need for new methods, and one under-explored option is the use of wearable cameras. This study tested the use of wearable cameras in PA measurement by comparing intensity classifications from accelerometers with wearable camera data. Seventy-eight 18–30-year-olds wore an Actigraph GT9X link accelerometer and Autographer wearable camera for three consecutive days. An image coding schedule was designed to assess activity categories and activity sub-categories defin

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Huang, Huiqun, Xi Yang, and Suining He. "Multi-Head Spatio-Temporal Attention Mechanism for Urban Anomaly Event Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (2021): 1–21. http://dx.doi.org/10.1145/3478099.

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Timely forecasting the urban anomaly events in advance is of great importance to the city management and planning. However, anomaly event prediction is highly challenging due to the sparseness of data, geographic heterogeneity (e.g., complex spatial correlation, skewed spatial distribution of anomaly events and crowd flows), and the dynamic temporal dependencies. In this study, we propose M-STAP, a novel Multi-head Spatio-Temporal Attention Prediction approach to address the problem of multi-region urban anomaly event prediction. Specifically, M-STAP considers the problem from three main aspec

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Hakim, Wahyu, Arief Achmad, and Chang-Wook Lee. "Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data." Remote Sensing 12, no. 21 (2020): 3627. http://dx.doi.org/10.3390/rs12213627.

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Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent

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