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About 10,000 papers for “data preprocessing”
Sien-Chong Wu, W. G. Melbourne · 1994
Improved technique for preprocessing data from Global Positioning System (GPS) receiver reduces processing time and number of data to be stored. Technique optimal in sense it maintains strength of data. Also sometimes increases ability to resolve ambiguities in numbers of cycles of received GPS carrier signals.
Cited by 0
K.-F. Doherr, G. Wulff, Maximilian Zollner · elib (German Aerospace Center) · 1993
Cited by 0
Vsevolod I. Medvedev, Dmitriy S. Sarychev, Alexey V. Skvortsov · CAD&GIS for roads · 2014
САПР Предварительная обработка данных мобильного лазерного сканирования в системе IndorCloudМедведев В.И., ведущий разработчик ООО «ИндорСофт» (г.Томск) Сарычев Д
Cited by 8Open access
KC Santosh, Rodrigue Rizk, Siddhi K. Bajracharya · Studies in computational intelligence · 2024
Cited by 0
Yuan Han (1267323), Wenhu Jin (22608205), Heng Liu (293917) et al. · Figshare · 2025
<div> This study addresses the inefficiencies in ecological restoration on the Qinghai-Tibet Plateau (QTP), particularly concerning prolonged vegetation restoration cycles, slow soil quality improvement, and difficulties in quantifying manual intervention measures. An integrated Cubist regression tree model is developed using ecological environment data from the QTP and multi-source environmental monitoring data from 2019 to 2023. The model combines a lightweight self-attention mechanism (SA) with bidirectional gated recurrent units (BiGRU) to enhance the accuracy and adaptability of restoration efficiency prediction. The SA mechanism dynamically adjusts environmental factor weights to strengthen nonlinear relationship capture capabilities, while the BiGRU learner optimizes temporal feature representation to accommodate spatiotemporal variability in restoration processes. Input factors include fractional vegetation cover (FVC), temperature, precipitation, soil moisture, and manual intervention measures (irrigation volume, planting density), with outputs being vegetation restoration rate and soil quality improvement effects. Experimental results demonstrate that the model achieves less than 5% error in vegetation restoration rate prediction, with correlation coefficients exceeding 0.90, and 96% accuracy in soil improvement prediction. Temperature and precipitation show contribution rates of 32% and 25%, respectively, while soil moisture and NDVI jointly contribute 25%. Prediction accuracy remains above 90% across different altitude zones, indicating strong regional adaptability. Notably, in areas with annual precipitation below 200 millimeters, every 10% increase in irrigation volume leads to approximately 15% improvement in vegetation survival rate. This study provides quantitative and operational intervention guidelines for plateau ecological restoration, enhances the evaluation efficiency of manual intervention measures, and has significant practical application value. </div>
Cited by 0Open access
Ha, Lauren, Diaz, Claudio · OSF Preprints (OSF Preprints) · 2022
Physical Activity Accelerometer Data Preprocessing
Cited by 1Open access
Jinyu Chen, Haoran Zhang, Wenjing Li et al. · Elsevier eBooks · 2022
Cited by 1
Ryosuke Muraki (12152308), Atsushi Teramoto (6338672), Keiko Sugimoto (129038) et al. · Figshare · 2022
<p>All frames of one cardiac cycle taken from the original echocardiography image were interpolated to 30 frames, and the ECG in the image was removed and trimmed.</p>
Cited by 0Open access
Christian Pozzorini (756064), Skander Mensi (756065), Olivier Hagens (756066) et al. · Figshare · 2015
<p><b>(A)</b> Schematic representation of the Active Electrode Compensation technique used to correct for the experimental bias known to occur when the same patch-clamp electrode is used to simultaneously inject and record from a single neuron. The artifactual voltage <i>V</i><sub>e</sub>(<i>t</i>) across the pipette is estimated by filtering the input current <i>I</i>(<i>t</i>) with the electrode filter <i>κ</i><sub>e</sub>(<i>t</i>). The intracellular membrane potential <i>V</i><sub>data</sub>(<i>t</i>) if finally obtained by subtracting the artifactual voltage <i>V</i><sub>e</sub>(<i>t</i>) from the recorded signal <i>V</i><sub>rec</sub>(<i>t</i>). <b>(B)</b> Typical optimal linear filter <i>κ</i><sub>opt</sub>(<i>t</i>) between the subthreshold input current <i>I</i><sub>sub</sub>(<i>t</i>) and the recorded signal <i>V</i><sub>sub</sub>(<i>t</i>). To estimate the electrode filter, an exponential fit is performed on the tail of <i>κ</i><sub>opt</sub>(<i>t</i>) (dashed black). Inset: Magnification of the y-axis illustrating the good accuracy of the exponential fit (dashed black) on the tail of the optimal linear filter <i>κ</i><sub>opt</sub>(<i>t</i>) (red). <b>(C)</b> Typical electrode filter <i>κ</i><sub>e</sub>(<i>t</i>) obtained by subtracting the exponential fit from the optimal linear filter <i>κ</i><sub>opt</sub>(<i>t</i>) (see panel <i>B</i>). Since <i>in vitro</i> recordings were performed with the standard bridge compensation technique, the electrode filter <i>κ</i><sub>e</sub>(<i>t</i>) is characterized by a strong initial negative peak. The characteristic timescale of the electrode filter <i>τ</i><sub>e</sub> was measured by performing an exponential fit (dashed black) on <i>κ</i><sub>e</sub>(<i>t</i>). Inset: distribution of the electrode timescales <i>τ</i><sub>e</sub> measured in ten different recordings included in this study. <b>(D)</b> Comparison between recorded signal <i>V</i><sub>rec</sub>(<i>t</i>) (black) and membrane potential <i>V</i><sub>data</sub>(<i>t</i>) (red) obtained after AEC. Inset: zoom indicating that AEC operates as a low-pass filter by removing high-frequency components from the acquired signal. Scale bars: 30 ms, 5 mV.</p>
Cited by 0Open access
Yi Guo (35817), Yangcheng Liu (13250220), Ting Zhou (38572) et al. · Figshare · 2023
<div><p>In recent years, deep learning has seen remarkable progress in many fields, especially with many excellent pre-training models emerged in Natural Language Processing(NLP). However, these pre-training models can not be used directly in music generation tasks due to the different representations between music symbols and text. Compared with the traditional presentation method of music melody that only includes the pitch relationship between single notes, the text-like representation method proposed in this paper contains more melody information, including pitch, rhythm and pauses, which expresses the melody in a form similar to text and makes it possible to use existing pre-training models in symbolic melody generation. In this paper, based on the generative pre-training-2(GPT-2) text generation model and transfer learning we propose MT-GPT-2(music textual GPT-2) model that is used in music melody generation. Then, a symbolic music evaluation method(MEM) is proposed through the combination of mathematical statistics, music theory knowledge and signal processing methods, which is more objective than the manual evaluation method. Based on this evaluation method and music theories, the music generation model in this paper are compared with other models (such as long short-term memory (LSTM) model,Leak-GAN model and Music SketchNet). The results show that the melody generated by the proposed model is closer to real music.</p></div>
Cited by 0Open access
Ehsan Yaghmaei (16985784), Albert Pierce (16985787), Hongxia Lu (6715001) et al. · Figshare · 2023
<div><p>Alzheimer’s disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer’s disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer’s Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.</p></div>
Cited by 0Open access
Yu Wang (12152), Fengzhu Sun (15206), Wei Lin (6475) et al. · Figshare · 2022
Cited by 0Open access
Bo Zhang (6559), Yijing Liang (6684968) · Figshare · 2025
<div> Air pollution control is an urgent problem in the field of environment, and it is crucial to accurately identify emission driving factors and collaborative emission reduction paths. In order to construct and analyze the driving mechanism of atmospheric pollutant emissions and explore the potential for regional collaborative emission reduction, an innovative three-stage progressive analysis framework was developed by combining Logarithmic Mean Divisia Index (LMDI) decomposition and Geographically Weighted Regression (GWR), which includes factor decomposition, spatial modeling, and collaborative optimization. Through empirical analysis, it was found that the energy intensity effect in Tangshan city reduces emissions by an average of −14.834 million tons per year, becoming the core driving force. The synergistic emission reduction ratio of SO2-PM2.5 in the Beijing Tianjin Hebei region reached 1: 0.38, with an average annual emission reduction of 297000 tons and a regional synergy index of 0.85 (<i><i>p</i></i> < 0.01), significantly better than other pollutant combinations. The adjusted R2 of the GWR model reached 0.86, the residual Moran’s I index was 0.07, and the proportion of significant variables reached 75%, which is 15.28% higher than other models. In addition, the Akaike information criterion corrected by the GWR model was reduced by an average of 12.78% compared to other models. The results indicated that the synergistic effect of multi factor decomposition and spatial heterogeneity analysis could significantly enhance the regional adaptability of emission reduction strategies, providing scientific support for cross regional collaborative governance. </div>
Cited by 0Open access
Miguel Ferreiro-Díaz, David Ruano-Ordás, Tomás R. Cotos-Yáñez et al. · 2019
Provide a tool to easily build customized data flows to pre-process large volumes of information from different sources. To this end, 'bdpar' allows to (i) easily use and create new functionalities and (ii) develop new data source extractors according to the user needs. Additionally, the package provides by default a predefined data flow to extract and pre-process the most relevant information (tokens, dates, ... ) from some textual sources (SMS, Email, YouTube comments).
Cited by 0Open access
Engy Fouda · Apress eBooks · 2024
This chapter shows some essential data cleaning and querying methods, such as comment statement, arithmetic, and comparison operators, and how to represent missing values and loops.
Cited by 0Open access
Olee Hoi Ying Lam, Susanne Tautenhahn, Gabriel Walther et al. · 2022
Designed to support the application of plant trait data providing easy applicable functions for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns and rows, excluding trait data according to different attributes, geocoding, long- to wide-table transformation, and data export. 'rtry' was initially developed as part of the TRY R project to preprocess trait data received via the TRY database.
Cited by 9Open access
Aki-Juhani Kyröläinen, Vincent Porretta · 2019
Cited by 4
Mauno Rönkkö, Jani Heikkinen, Ville Kotovirta et al. · Future Generation Computer Systems · 2014
Cited by 14
Amjad Iqbal (3997619), Rashid Amin (1389156), Faisal S. Alsubaei (18778714) et al. · Figshare · 2024
<div><p>Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.</p></div>
Cited by 0Open access
Giskeødegård, Guro F., Bathen, Tone Frost, Euceda, Leslie R. · Duo Research Archive (University of Oslo) · 2015
Metabolomics involves the large scale analysis of metabolites and thus, provides information regarding cellular processes in a biological sample. Independently of the analytical technique used, a vast amount of data is always acquired when carrying out metabolomics studies; this results in complex datasets with large amounts of variables. This type of data requires multivariate statistical analysis for its proper biological interpretation. Prior to multivariate analysis, preprocessing of the data must be carried out to remove unwanted variation such as instrumental or experimental artifacts. This review aims to outline the steps in the preprocessing of NMR metabolomics data and describe some of the methods to perform these. Since using different preprocessing methods may produce different results, it is important that an appropriate pipeline exists for the selection of the optimal combination of methods in the preprocessing workflow.
Cited by 0Open access