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Journal articles on the topic 'Dataset IMPACT'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Althnian, Alhanoof, Duaa AlSaeed, Heyam Al-Baity, et al. "Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain." Applied Sciences 11, no. 2 (2021): 796. http://dx.doi.org/10.3390/app11020796.

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Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performanc

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Tarek, Mostafa, François P. Brissette, and Richard Arsenault. "Large-Scale Analysis of Global Gridded Precipitation and Temperature Datasets for Climate Change Impact Studies." Journal of Hydrometeorology 21, no. 11 (2020): 2623–40. http://dx.doi.org/10.1175/jhm-d-20-0100.1.

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AbstractCurrently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be

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Khreis, Haneen, Kees de Hoogh, Josias Zietsman, and Mark J. Nieuwenhuijsen. "The Impact of Different Validation Datasets on Air Quality Modeling Performance." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 25 (2018): 57–66. http://dx.doi.org/10.1177/0361198118780682.

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Many studies rely on air pollution modeling such as land use regression (LUR) or atmospheric dispersion (AD) modeling in epidemiological and health impact assessments. Generally, these models are only validated using one validation dataset and their estimates at select receptor points are generalized to larger areas. The primary objective of this paper was to explore the effect of different validation datasets on the validation of air quality models. The secondary objective was to explore the effect of the model estimates’ spatial resolution on the models’ validity at different locations. Annu

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Xu, Hanzhang, Yaolin Pei, Matthew Dupre, and Bei Wu. "Existing Datasets to Study the Impact of Internal Migration on Caregiving Arrangements." Innovation in Aging 4, Supplement_1 (2020): 750. http://dx.doi.org/10.1093/geroni/igaa057.2703.

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Abstract Massive rural-to-urban migration in China has a significant impact on caregiving arrangements among Chinese older adults. To stimulate research on the intersection of migration and caregiving, we conducted an inventory of longitudinal aging survey datasets that included older adults from mainland China. Large public available datasets that included measures related to migration and caregiving were searched and reviewed for eligibility. Key characteristics of each dataset, including study design, sample size, and measures, were extracted. Seven eligible datasets were identified, and fi

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Tarek, Mostafa, François Brissette, and Richard Arsenault. "Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies." Hydrology and Earth System Sciences 25, no. 6 (2021): 3331–50. http://dx.doi.org/10.5194/hess-25-3331-2021.

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Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used

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Zhan, Xianghao, Yiheng Li, Yuzhe Liu, et al. "The relationship between brain injury criteria and brain strain across different types of head impacts can be different." Journal of The Royal Society Interface 18, no. 179 (2021): 20210260. http://dx.doi.org/10.1098/rsif.2021.0260.

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Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal

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Antuña, J. C., J. A. Añel, and L. Gimeno. "Impact of missing sounding reports on mandatory levels and tropopause statistics: a case study." Annales Geophysicae 24, no. 10 (2006): 2445–49. http://dx.doi.org/10.5194/angeo-24-2445-2006.

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Abstract. This paper describes the effect of missing sounding reports on temperature and pressure mean values for mandatory levels using the aerological information from the Camagüey Meteorological Centre. Also it is described the effect of missing data on mean temperature and pressure values at the multiple tropopause levels. The case study belongs to one station for a time lag of eight years. Up to the present these types of studies have been conducted using simulated datasets. The present one uses a real inhomogeneous radiosonde dataset. The main reason for missing reports were transmission

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Van Hulse, Jason, Taghi M. Khoshgoftaar, and Amri Napolitano. "Evaluating the Impact of Data Quality on Sampling." Journal of Information & Knowledge Management 10, no. 03 (2011): 225–45. http://dx.doi.org/10.1142/s021964921100295x.

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Learning from imbalanced training data can be a difficult endeavour, and the task is made even more challenging if the data is of low quality or the size of the training dataset is small. Data sampling is a commonly used method for improving learner performance when data is imbalanced. However, little effort has been put forth to investigate the performance of data sampling techniques when data is both noisy and imbalanced. In this work, we present a comprehensive empirical investigation of the impact of changes in four training dataset characteristics — dataset size, class distribution, noise

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Lebedev, Iliya. "Dataset segmentation considering the information about impact factors." Information and Control Systems, no. 3 (June 29, 2021): 29–38. http://dx.doi.org/10.31799/1684-8853-2021-3-29-38.

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Introduction: The application of machine learning methods involves the collection and processing of data which comes from the recording elements in the offline mode. Most models are trained on historical data and then used in forecasting, classification, search for influencing factors or impacts, and state analysis. In the long run, the data value ranges can change, affecting the quality of the classification algorithms and leading to the situation when the models should be constantly trained or readjusted taking into account the input data. Purpose: Development of a technique to improve the q

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Drusch, Matthias. "Sea Ice Concentration Analyses for the Baltic Sea and Their Impact on Numerical Weather Prediction." Journal of Applied Meteorology and Climatology 45, no. 7 (2006): 982–94. http://dx.doi.org/10.1175/jam2376.1.

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Abstract Sea ice concentration plays a fundamental role in the exchange of water and energy between the ocean and the atmosphere. Global real-time datasets of sea ice concentration are based on satellite observations, which do not necessarily resolve small-scale patterns or coastal features. In this study, the global National Centers for Environmental Prediction (NCEP) 0.5° sea ice concentration dataset is compared with a regional high-resolution analysis for the Baltic Sea produced 2 times per week by the Swedish Meteorological and Hydrological Institute (SMHI). In general, the NCEP dataset e

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Alhumaima, A. S., and S. M. Abdullaev. "Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data." Earth Interactions 24, no. 7 (2020): 1–18. http://dx.doi.org/10.1175/ei-d-20-0002.1.

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AbstractThe primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the lower Tigris basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen–Trewartha bioclimatic landscapes with the positions of botanical districts, land-cover types, and streamfl

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Swathi, Kailasam, and Bobba Basaveswara Rao. "Impact of PDS Based kNN Classifiers on Kyoto Dataset." International Journal of Rough Sets and Data Analysis 6, no. 2 (2019): 61–72. http://dx.doi.org/10.4018/ijrsda.2019040105.

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This article compares the performance of different Partial Distance Search-based (PDS) kNN classifiers on a benchmark Kyoto 2006+ dataset for Network Intrusion Detection Systems (NIDS). These PDS classifiers are named based on features indexing. They are: i) Simple PDS kNN, the features are not indexed (SPDS), ii) Variance indexing based kNN (VIPDS), the features are indexed by the variance of the features, and iii) Correlation coefficient indexing-based kNN (CIPDS), the features are indexed by the correlation coefficient of the features with a class label. For comparative study between these

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Burrell, Arden L., Jason P. Evans, and Yi Liu. "The impact of dataset selection on land degradation assessment." ISPRS Journal of Photogrammetry and Remote Sensing 146 (December 2018): 22–37. http://dx.doi.org/10.1016/j.isprsjprs.2018.08.017.

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Hait, Amy, and Susan E. Powers. "Dataset: Feminine hygiene product lifecycle inventory and impact assessment." Data in Brief 28 (February 2020): 104851. http://dx.doi.org/10.1016/j.dib.2019.104851.

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Marone, Federica, Jakob Vogel, and Marco Stampanoni. "Impact of lossy compression of X-ray projections onto reconstructed tomographic slices." Journal of Synchrotron Radiation 27, no. 5 (2020): 1326–38. http://dx.doi.org/10.1107/s1600577520007353.

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Modern detectors used at synchrotron tomographic microscopy beamlines typically have sensors with more than 4–5 mega-pixels and are capable of acquiring 100–1000 frames per second at full frame. As a consequence, a data rate of a few TB per day can easily be exceeded, reaching peaks of a few tens of TB per day for time-resolved tomographic experiments. This data needs to be post-processed, analysed, stored and possibly transferred, imposing a significant burden onto the IT infrastructure. Compression of tomographic data, as routinely done for diffraction experiments, is therefore highly desira

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Lu, Zhijian, Gang Liu, and Rongwen Liao. "A pseudo Karnaugh mapping approach for datasets imbalance." E3S Web of Conferences 236 (2021): 04006. http://dx.doi.org/10.1051/e3sconf/202123604006.

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The problem of dataset imbalance has raised a wide concern in many machine learning areas, but not in non-intrusive load monitoring, or load disaggregation. In this study, a pictorial evaluation method is proposed to representation the imbalance class distribution in datasets. We colored a Karnaugh maps according to the quantities of different variables combination to offer a visual impact to the whole dataset. After utilizing this method on a public dataset and its testing result, a clear imbalanced abundance in the dataset and an exciting performance have been found. A preliminary Python pac

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Elsner, Marketa M., Subhrendu Gangopadhyay, Tom Pruitt, Levi D. Brekke, Naoki Mizukami, and Martyn P. Clark. "How Does the Choice of Distributed Meteorological Data Affect Hydrologic Model Calibration and Streamflow Simulations?" Journal of Hydrometeorology 15, no. 4 (2014): 1384–403. http://dx.doi.org/10.1175/jhm-d-13-083.1.

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Abstract Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly incorporated into modeling efforts for long-term natural resources planning. For water management decisions, it is critical to understand the uncertainty associated with the different choices made in hydrologic impact assessments (choice of hydrologic model, choice of forcing dataset, calibration strategy, etc.). This paper evaluates differences among four commonly used historical meteorological datasets and their impacts on streamflow simulations produced using the Variable Infiltrat

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Famien, Adjoua Moise, Serge Janicot, Abe Delfin Ochou, et al. "A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies." Earth System Dynamics 9, no. 1 (2018): 313–38. http://dx.doi.org/10.5194/esd-9-313-2018.

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Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortw

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Eichhorn, Marcus, Mattes Scheftelowitz, Matthias Reichmuth, et al. "Spatial Distribution of Wind Turbines, Photovoltaic Field Systems, Bioenergy, and River Hydro Power Plants in Germany." Data 4, no. 1 (2019): 29. http://dx.doi.org/10.3390/data4010029.

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The expansion of renewable energy technologies, accompanied by an increasingly decentralized supply structure, raises many research questions regarding the structure, dimension, and impacts of the electricity supply network. In this context, information on renewable energy plants, particularly their spatial distribution and key parameters—e.g., installed capacity, total size, and required space—are more and more important for public decision makers and different scientific domains, such as energy system analysis and impact assessment. The dataset described in this paper covers the spatial dist

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Utomo, Fandy Setyo, Nanna Suryana, and Mohd Sanusi Azmi. "STEMMING IMPACT ANALYSIS ON INDONESIAN QURAN TRANSLATION AND THEIR TAFSIR CLASSIFICATION FOR ONTOLOGY INSTANCES." IIUM Engineering Journal 21, no. 1 (2020): 33–50. http://dx.doi.org/10.31436/iiumej.v21i1.1170.

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The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming imp

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Huda, Noor Ul, Bolette D. Hansen, Rikke Gade, and Thomas B. Moeslund. "The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection." Sensors 20, no. 7 (2020): 1982. http://dx.doi.org/10.3390/s20071982.

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Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenom

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Abuaiadah, Diab, Jihad El Sana, and Walid Abusalah. "On the Impact of Dataset Characteristics on Arabic Document Classification." International Journal of Computer Applications 101, no. 7 (2014): 31–38. http://dx.doi.org/10.5120/17701-8680.

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Imlawi, Jehad, and Mohammad Alsharo. "Evaluating classification accuracy: the impact of resampling and dataset size." International Journal of Business Information Systems 24, no. 1 (2017): 91. http://dx.doi.org/10.1504/ijbis.2017.080947.

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Imlawi, Jehad, and Mohammad Alsharo. "Evaluating classification accuracy: the impact of resampling and dataset size." International Journal of Business Information Systems 24, no. 1 (2017): 91. http://dx.doi.org/10.1504/ijbis.2017.10000821.

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Fraga, Helder, Daniel Molitor, Luisa Leolini, and João A. Santos. "What Is the Impact of Heatwaves on European Viticulture? A Modelling Assessment." Applied Sciences 10, no. 9 (2020): 3030. http://dx.doi.org/10.3390/app10093030.

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Extreme heat events or heatwaves can be particularly harmful to grapevines, posing a major challenge to winegrowers in Europe. The present study is focused on the application of the crop model STICS to assess the potential impacts of heatwaves over some of the most renowned winemaking regions in Europe. For this purpose, STICS was applied to grapevines, using high-resolution weather, soil and terrain datasets from 1986 to 2015. To assess the impact of heatwaves, the weather dataset was artificially modified, generating periods with anomalously high temperatures (+5 °C), at specific onset dates

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Cuceloglu, Gokhan, and Izzet Ozturk. "Assessing the Impact of CFSR and Local Climate Datasets on Hydrological Modeling Performance in the Mountainous Black Sea Catchment." Water 11, no. 11 (2019): 2277. http://dx.doi.org/10.3390/w11112277.

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Precise representation of precipitation input is one of the predominant factors affecting the simulation of hydrological processes in catchments. Choosing the representative climate datasets is crucial to obtain accurate model results, especially in mountainous regions. Hence, this study assesses the suitability of the Climate Forecasting System Reanalysis (CFSR) and local climate data to simulate the streamflow at multiple gauges in the data-scarce mountainous Black Sea catchment. Moreover, the applicability of using the elevations band in the model is also tested. The Soil and Water Assessme

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Jorgeson, Ian A., Ryan P. Breslawski, and Abigail E. Fisher. "Radiocarbon simulation fails to support the temporal synchroneity requirement of the Younger Dryas impact hypothesis." Quaternary Research 96 (February 13, 2020): 123–39. http://dx.doi.org/10.1017/qua.2019.83.

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AbstractFine-scale temporal processes, such as the synchronous deposition of organic materials, can be challenging to identify using 14C datasets. While some events, such as volcanic eruptions, leave clear evidence for synchronous deposition, synchroneity is more difficult to establish for other types of events. This has been a source of controversy regarding 14C dates associated with a hypothesized extraterrestrial impact at the Younger Dryas Boundary (YDB). To address this controversy, we first aggregate 14C measurements from Northern Hemisphere YDB sites. We also aggregate 14C measurements

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Dass, Gaurhari, Manh-Tu Vu, Pan Xu, et al. "The omics discovery REST interface." Nucleic Acids Research 48, W1 (2020): W380—W384. http://dx.doi.org/10.1093/nar/gkaa326.

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Abstract The Omics Discovery Index is an open source platform that can be used to access, discover and disseminate omics datasets. OmicsDI integrates proteomics, genomics, metabolomics, models and transcriptomics datasets. Using an efficient indexing system, OmicsDI integrates different biological entities including genes, transcripts, proteins, metabolites and the corresponding publications from PubMed. In addition, it implements a group of pipelines to estimate the impact of each dataset by tracing the number of citations, reanalysis and biological entities reported by each dataset. Here, we

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Gampe, David, Josef Schmid, and Ralf Ludwig. "Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of Precipitation." Journal of Hydrometeorology 20, no. 9 (2019): 1813–28. http://dx.doi.org/10.1175/jhm-d-18-0108.1.

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Abstract Gridded datasets of precipitation are of great importance to evaluate recent climate models and are frequently applied to select a subset of available models. As climate models are still prone to biases on the regional scale, gridded datasets are also essential to correct or adjust these biases. Various studies revealed considerable differences, that is, observational uncertainty, in the available gridded datasets of precipitation, especially over complex terrain. This study focuses on the impacts of observational uncertainty on the evaluation, selection, and bias correction of 15 reg

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Jittawiriyanukoon, Chanintorn. "Granularity analysis of classification and estimation for complex datasets with MOA." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 409. http://dx.doi.org/10.11591/ijece.v9i1.pp409-416.

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<span>Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently sc

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Waimberg, Joshua, Lindsay K. Cloud, Andrew T. Campbell, Ruth Lindberg, and Keshia M. Pollack. "Tracking State-Level Health Impact Assessment Legislation from 2012-2016." Chronicles of Health Impact Assessment 3, no. 1 (2018): 1–10. http://dx.doi.org/10.18060/22249.

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Background: To collect and analyze state-level Health Impact Assessment (HIA) legislative activity. Methods: Two longitudinal datasets examining state-level HIA legislation across the United States between January 1, 2012 and December 31, 2016 were developed using the policy surveillance process. One dataset captures the characteristics of all HIA bills that were introduced and failed, or introduced and were still under consideration, during the time period of the study; the second dataset captures the characteristics of all HIA laws that were enacted or amended, including statutes and r

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Grannis, Shaun J., Huiping Xu, Joshua R. Vest, et al. "Evaluating the effect of data standardization and validation on patient matching accuracy." Journal of the American Medical Informatics Association 26, no. 5 (2019): 447–56. http://dx.doi.org/10.1093/jamia/ocy191.

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Abstract Objective This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets. Materials and Methods We used 4 manually reviewed datasets, containing a random selection of matches and nonmatches. Matching datasets included health information exchange (HIE) records, public health registry records, Social Security Death Master File records, and newborn screening records. Standardized fields including last name, telephone number, social security number, date of birth, and address. Matching performance w

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Escribano, Jerónimo, Olivier Boucher, Frédéric Chevallier, and Nicolás Huneeus. "Impact of the choice of the satellite aerosol optical depth product in a sub-regional dust emission inversion." Atmospheric Chemistry and Physics 17, no. 11 (2017): 7111–26. http://dx.doi.org/10.5194/acp-17-7111-2017.

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Abstract. Mineral dust is the major continental contributor to the global atmospheric aerosol burden with important effects on the climate system. Regionally, a large fraction of the emitted dust is produced in northern Africa; however, the total emission flux from there is still highly uncertain. In order to reduce these uncertainties, emission estimates through top-down approaches (i.e. usually models constrained by observations) have been successfully developed and implemented. Such studies usually rely on a single observational dataset and propagate the possible observational errors of thi

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Dlamini, Nkosikhona, and Terence L. van Zyl. "Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification." Sensors 21, no. 18 (2021): 6109. http://dx.doi.org/10.3390/s21186109.

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Similarity learning using deep convolutional neural networks has been applied extensively in solving computer vision problems. This attraction is supported by its success in one-shot and zero-shot classification applications. The advances in similarity learning are essential for smaller datasets or datasets in which few class labels exist per class such as wildlife re-identification. Improving the performance of similarity learning models comes with developing new sampling techniques and designing loss functions better suited to training similarity in neural networks. However, the impact of th

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Cucchi, Marco, Graham P. Weedon, Alessandro Amici, et al. "WFDE5: bias-adjusted ERA5 reanalysis data for impact studies." Earth System Science Data 12, no. 3 (2020): 2097–120. http://dx.doi.org/10.5194/essd-12-2097-2020.

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Abstract. The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5∘ spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological obser

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Tiotsop, Lohic Fotio, Antonio Servetti, and Enrico Masala. "Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset." Electronic Imaging 2020, no. 11 (2020): 93–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-093.

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Large subjectively annotated datasets are crucial to the development and testing of objective video quality measures (VQMs). In this work we focus on the recently released ITS4S dataset. Relying on statistical tools, we show that the content of the dataset is rather heterogeneous from the point of view of quality assessment. Such diversity naturally makes the dataset a worthy asset to validate the accuracy of video quality metrics (VQMs). In particular we study the ability of VQMs to model the reduction or the increase of the visibility of distortion due to the spatial activity in the content.

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Guo, Lin Lawrence, Stephen R. Pfohl, Jason Fries, et al. "Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine." Applied Clinical Informatics 12, no. 04 (2021): 808–15. http://dx.doi.org/10.1055/s-0041-1735184.

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Abstract Objective The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was m

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Zakria, Jianhua Deng, Jingye Cai, Muhammad Umar Aftab, Muhammad Saddam Khokhar, and Rajesh Kumar. "Visual Features with Spatio-Temporal-Based Fusion Model for Cross-Dataset Vehicle Re-Identification." Electronics 9, no. 7 (2020): 1083. http://dx.doi.org/10.3390/electronics9071083.

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Vehicle re-identification (Re-Id) is the key module in an intelligent transportation system (ITS). Due to its versatile applicability in metropolitan cities, this task has received increasing attention these days. It aims to identify whether the specific vehicle has already appeared over the surveillance network or not. Mostly, the vehicle Re-Id method are evaluated on a single dataset, in which training and testing of the model is performed on the same dataset. However in practice, this negatively effects model generalization ability due to biased datasets along with the significant differenc

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KC, Kamal, Zhendong Yin, Dasen Li, and Zhilu Wu. "Impacts of Background Removal on Convolutional Neural Networks for Plant Disease Classification In-Situ." Agriculture 11, no. 9 (2021): 827. http://dx.doi.org/10.3390/agriculture11090827.

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Convolutional neural networks have an immense impact on computer vision tasks. However, the accuracy of convolutional neural networks on a dataset is tremendously affected when images within the dataset highly vary. Test images of plant leaves are usually taken in situ. These images, apart from the region of interest, contain unwanted parts of plants, soil, rocks, and/or human body parts. Segmentation helps isolate the target region and a deep convolutional neural network classifies images precisely. Therefore, we combined edge and morphological based segmentation, background subtraction, and

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Kranjčevičová, Anita, Eva Kašná, Michaela Brzáková, Josef Přibyl, and Luboš Vostrý. "Impact of reference population and marker density on accuracy of population imputation." Czech Journal of Animal Science 64, No. 10 (2019): 405–10. http://dx.doi.org/10.17221/148/2019-cjas.

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The effect of the reference population size and the number of missing single nucleotide polymorphisms (SNPs) on imputation accuracy was determined. The population imputation method using the FImpute software was applied. The dataset used for the purpose of this study was taken from the database of the Holstein Cattle Breeders Association of the Czech Republic. It contains 1000 animals genotyped with the Illumina BovineSNP50 v.2 BeadChip. Two datasets were created, the first containing the original genotypes, including the missing SNPs, the second containing the same genotypes modified to avoid

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Schuler, Thomas Vikhamar, and Torbjørn Ims Østby. "Sval_Imp: a gridded forcing dataset for climate change impact research on Svalbard." Earth System Science Data 12, no. 2 (2020): 875–85. http://dx.doi.org/10.5194/essd-12-875-2020.

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Abstract. We present Sval_Imp, a high-resolution gridded dataset designed for forcing models of terrestrial surface processes on Svalbard. The dataset is defined on a 1 km grid covering the archipelago of Svalbard, located in the Norwegian Arctic (74–82∘ N). Using a hybrid methodology, combining multidimensional interpolation with simple dynamical modeling, the atmospheric reanalyses ERA-40 and ERA-Interim by the European Centre for Medium-Range Weather Forecasting have been downscaled to cover the period 1957–2017 at steps of 6 h. The dataset is publicly available from a data repository. In t

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Chen, Jian-Min, Jin-Huan Lin, Emmanuelle Masson, et al. "The Experimentally Obtained Functional Impact Assessments of 5' Splice Site GT>GC Variants Differ Markedly from Those Predicted." Current Genomics 21, no. 1 (2020): 56–66. http://dx.doi.org/10.2174/1389202921666200210141701.

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Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology: Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three

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Feeney, Kevin Chekov, Declan O'Sullivan, Wei Tai, and Rob Brennan. "Improving Curated Web-Data Quality with Structured Harvesting and Assessment." International Journal on Semantic Web and Information Systems 10, no. 2 (2014): 35–62. http://dx.doi.org/10.4018/ijswis.2014040103.

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This paper describes a semi-automated process, framework and tools for harvesting, assessing, improving and maintaining high-quality linked-data. The framework, known as DaCura1, provides dataset curators, who may not be knowledge engineers, with tools to collect and curate evolving linked data datasets that maintain quality over time. The framework encompasses a novel process, workflow and architecture. A working implementation has been produced and applied firstly to the publication of an existing social-sciences dataset, then to the harvesting and curation of a related dataset from an unstr

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Marson, Rebecca, David McD Taylor, Karen Ashby, and Erin Cassell. "Victorian Emergency Minimum Dataset: Factors that impact upon the data quality." Emergency Medicine Australasia 17, no. 2 (2005): 104–12. http://dx.doi.org/10.1111/j.1742-6723.2005.00700.x.

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Suresh, A., and A. Jayanthila Devi. "Integrating the power of social media dataset impact in medical diagnosis." International Journal of Intelligent Enterprise 6, no. 1 (2019): 53. http://dx.doi.org/10.1504/ijie.2019.100034.

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Devi, A. Jayanthila, and A. Suresh. "Integrating the power of social media dataset impact in medical diagnosis." International Journal of Intelligent Enterprise 6, no. 1 (2019): 53. http://dx.doi.org/10.1504/ijie.2019.10021617.

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Kumar, Shreya, and Swarnalaxmi Thiruvenkadam. "An Analysis of the Impact of Spectral Contrast Feature in Speech Emotion Recognition." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 9, no. 2 (2021): 87. http://dx.doi.org/10.3991/ijes.v9i2.22983.

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Feature extraction is an integral part in speech emotion recognition. Some emotions become indistinguishable from others due to high resemblance in their features, which results in low prediction accuracy. This paper analyses the impact of spectral contrast feature in increasing the accuracy for such emotions. The RAVDESS dataset has been chosen for this study. The SAVEE dataset, CREMA-D dataset and JL corpus dataset were also used to test its performance over different English accents. In addition to that, EmoDB dataset has been used to study its performance in the German language. The use of

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García Gutiérrez, Isabel, Daniel Elduque, Carmelo Pina, Rafael Tobajas, and Carlos Javierre. "Influence of the Composition on the Environmental Impact of a Casting Magnesium Alloy." Sustainability 12, no. 24 (2020): 10494. http://dx.doi.org/10.3390/su122410494.

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The influence of the composition of magnesium alloys on their environmental impact was analyzed. In order to perform a more accurate environmental impact calculation, life cycle assessment (LCA) with the ReCiPe 2016 Endpoint and IPCC 2013 GWP (100 y) methodology was used, taking the EcoInvent AZ91 magnesium alloy dataset as reference. This dataset has been updated with the material composition range of several alloys included in the European standard EN 1753:2019. The balanced, maximum, and minimum environmental impact values were obtained. In general, the overall impact of the studied magnesi

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Peng, Zirong, Yifeng Lu, Constantinos Hatzoglou, et al. "An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography." Microscopy and Microanalysis 25, no. 2 (2019): 389–400. http://dx.doi.org/10.1017/s1431927618016112.

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AbstractWe introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrat

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Wu, T., B. Vallet, M. Pierrot-Deseilligny, and E. Rupnik. "A NEW STEREO DENSE MATCHING BENCHMARK DATASET FOR DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 405–12. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-405-2021.

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Abstract. Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper,

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