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Journal articles on the topic 'Data combination'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Ting, Kai Ming, Boon Toh Low, and Ian H. Witten. "Learning from Batched Data: Model Combination Versus Data Combination." Knowledge and Information Systems 1, no. 1 (1999): 83–106. http://dx.doi.org/10.1007/bf03325092.

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Dong, Xiuze, Yunchuan Guo, Fenghua Li, Liju Dong, and Arshad Khan. "Combination Model of Heterogeneous Data for Security Measurement." JUCS - Journal of Universal Computer Science 25, no. (3) (2019): 270–81. https://doi.org/10.3217/jucs-025-03-0270.

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Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult to classify and combine them on demand. In this paper, considering implication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model.

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3

Yin Liu, Yin Liu, and Yang Zhang Yin Liu. "A Weighted Evidence Combination Method for Multisensor Data Fusion." 網際網路技術學刊 23, no. 3 (2022): 553–60. http://dx.doi.org/10.53106/160792642022052303013.

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<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including

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Zagidullin, Bulat, Jehad Aldahdooh, Shuyu Zheng, et al. "DrugComb: an integrative cancer drug combination data portal." Nucleic Acids Research 47, W1 (2019): W43—W51. http://dx.doi.org/10.1093/nar/gkz337.

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AbstractDrug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi)

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Doneus, M., and W. Neubauer. "2D combination of prospection data." Archaeological Prospection 5, no. 1 (1998): 29–56. http://dx.doi.org/10.1002/(sici)1099-0763(199803)5:1<29::aid-arp92>3.0.co;2-t.

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6

Zhao, Wei, Kris Sachsenmeier, Lanju Zhang, Erin Sult, Robert E. Hollingsworth, and Harry Yang. "A New Bliss Independence Model to Analyze Drug Combination Data." Journal of Biomolecular Screening 19, no. 5 (2014): 817–21. http://dx.doi.org/10.1177/1087057114521867.

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The Bliss independence model is widely used to analyze drug combination data when screening for candidate drug combinations. The method compares the observed combination response ( YO) with the predicted combination response ( YP), which was obtained based on the assumption that there is no effect from drug-drug interactions. Typically, the combination effect is declared synergistic if YO is greater than YP. However, this method lacks statistical rigor because it does not take into account the variability of the response measures and can frequently cause false-positive claims. In this article,

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Tang, Jing. "DrugComb - an integrative cancer drug combination data portal (v1.5)." Nucleic Acids Res. 2021 49, W1 (2025): W174—W184. https://doi.org/10.1093/nar/gkab438.

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Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.org) where the r

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8

Buxton, B. F., W. B. Langdon, and S. J. Barrett. "Data Fusion by Intelligent Classifier Combination." Measurement and Control 34, no. 8 (2001): 229–34. http://dx.doi.org/10.1177/002029400103400802.

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9

Bhattacharya, Debopam. "Evaluating treatment protocols using data combination." Journal of Econometrics 173, no. 2 (2013): 160–74. http://dx.doi.org/10.1016/j.jeconom.2012.11.003.

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Valton, Kamberaj. "CATEGORICAL DATA ANALYSIS USING LOGISTIC REGRESSION." https://link.springer.com/journal/10898 3 (September 7, 2021): 11. https://doi.org/10.5281/zenodo.5534800.

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The categorized data that will be analyzed in this paper will be of the type that will use the logistic regression method. The rest of the paper will focus on logistic regression and then the combination of these in the title of the topic mentioned above. Logistic regression is a technique widely used for categorical data analysis, offering increased flexibility compared to traditional intersection analysis. A binary result can be predicted using one or more categorical variables, continuous variables or combinations.

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11

Kuacharoen, Pramote. "Combination of Data Masking and Data Encryption for Cloud Database." Applied Mechanics and Materials 571-572 (June 2014): 617–20. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.617.

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Database as a service in cloud computing enables the user to create, store, modify, and retrieve data over the Internet. The user does not have to install and maintain the database himself. Instead, the database service provider takes responsibility for installing and maintaining the database. Storing data in a cloud database introduces security risks in data confidentiality, data integrity, and privacy. This research aims to design a method for storing data in a cloud database that provides data confidentiality and privacy and allows querying data. The comparison operations and aggregate func

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12

Bolarinwa, Bushirat T., and Ismaila A. Bolarinwa. "Single Versus Combined Forecasting: The Case Of Wind Run Data." European Scientific Journal, ESJ 17, no. 21 (2021): 189. http://dx.doi.org/10.19044/esj.2021.v17n21p189.

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This article compared single to combined forecasts of wind run using artificial neural networks, decomposition, Holt-Winters’ and SARIMA models. The artificial neural networks utilized the feedback framework while decomposition and Holt-Winters’ approaches utilized their multiplicative versions. Holt-Winters’ performed best of single models but ranked fourth, of all fifteen models (single and combined). The combination of decomposition and Holt-Winters’ models ranked best of all two-model combinations and second of all models. Combination of decomposition, Holt-Winters’ and SARIMA performed be

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Dr., G. A. Mylavathi, and B. Srinivasan Dr. "A SURVEY ON BIG DATA ANALYSIS METHODS AND USAGE." IJRSET JUNE Volume 9 Issue 6 9, no. 6 (2022): 28–32. https://doi.org/10.5281/zenodo.6793590.

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The purpose of this study is to investigate the sensitivity of contrast-based textural measurements and morphological characteristics that derive from high-resolution satellite imagery when diverse image enhancements techniques are piloted. The general framework of the application is the built-up/no built-up detection. In the existence of a low-resolution reference layer, we apply supervised learning that indirectly reduces the uncertainty and improves the quality of the reference layer. Based on the new class label assignments, the image histogram is adjusted suitably for the computation of c

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14

Zhang, Zuowei, Songtao Ye, Yiru Zhang, Weiping Ding, and Hao Wang. "Belief Combination of Classifiers for Incomplete Data." IEEE/CAA Journal of Automatica Sinica 9, no. 4 (2022): 652–67. http://dx.doi.org/10.1109/jas.2022.105458.

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15

Willett, Peter. "Combination of Similarity Rankings Using Data Fusion." Journal of Chemical Information and Modeling 53, no. 1 (2013): 1–10. http://dx.doi.org/10.1021/ci300547g.

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16

Sancetta, Alessio. "RECURSIVE FORECAST COMBINATION FOR DEPENDENT HETEROGENEOUS DATA." Econometric Theory 26, no. 2 (2009): 598–631. http://dx.doi.org/10.1017/s0266466609100105.

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This paper studies a procedure to combine individual forecasts that achieve theoretical optimal performance. The results apply to a wide variety of loss functions and only require a tail condition on the data sequences. The theoretical results show that the bounds are also valid in the case of time varying combination weights.

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17

Oza, Markand P. "Selection of band combination for IRS data." Journal of the Indian Society of Remote Sensing 17, no. 2 (1989): 23–26. http://dx.doi.org/10.1007/bf02991906.

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18

Leith, D. J., D. J. Murray-Smith, and R. Bradley. "Combination of data sets for system identification." IEE Proceedings D Control Theory and Applications 140, no. 1 (1993): 11. http://dx.doi.org/10.1049/ip-d.1993.0002.

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19

Pitcher, E. M., P. H. Stevens, E. R. Davies, P. R. Goddard, and P. C. Jackson. "Transfer and combination of digital image data." British Journal of Radiology 58, no. 692 (1985): 701–3. http://dx.doi.org/10.1259/0007-1285-58-692-701.

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20

Hu, Mingming, and Haiyan Song. "Data source combination for tourism demand forecasting." Tourism Economics 26, no. 7 (2019): 1248–65. http://dx.doi.org/10.1177/1354816619872592.

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Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Maca

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21

He, Jian-Wei, Wen-Kai Lu, and Zhong-Xiao Li. "An adaptive over/under data combination method." Applied Geophysics 10, no. 4 (2013): 469–76. http://dx.doi.org/10.1007/s11770-013-0397-x.

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22

Hu, X., X. Liao, and C. Huang. "Data weighting and solution assessment in combination." Journal of Geodesy 73, no. 8 (1999): 391–97. http://dx.doi.org/10.1007/s001900050258.

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23

Amro, Lubna, Frank Konietschke, and Markus Pauly. "Multiplication‐combination tests for incomplete paired data." Statistics in Medicine 38, no. 17 (2019): 3243–55. http://dx.doi.org/10.1002/sim.8178.

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24

Qian, Yuntao, Ching Y. Suen, and Yuanyan Tang. "Sequential combination methods for data clustering analysis." Journal of Computer Science and Technology 17, no. 2 (2002): 118–28. http://dx.doi.org/10.1007/bf02962204.

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25

Galasso, Gabriele, Gianniantonio Domina, Nicola M.G. Ardenghi, et al. "Notulae to the Italian alien vascular flora: 3." Italian Botanist 3 (May 17, 2017): 49–71. https://doi.org/10.3897/ib.3.13126.

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In this contribution, new data concerning the Italian distribution of alien vascular flora are presented. It includes new records, exclusions, confirmations, and status changes for Italy or for Italian administrative regions for taxa in the genera Acer, Amaranthus, Araujia, Aubrieta, Avena, Bidens, Calycanthus, Celtis, Elaeagnus, Eragrostis, Euonymus, Fallopia, Ficus, Hedera, Lantana, Ligustrum, Ludwigia, Morus, Oenothera, Opuntia, Oxalis, Parkinsonia, Paspalum, Paulownia, Platycladus, Pleuropterus, Rumex, Salvia, Senecio, Setaria, Syagrus, Tradescantia, Trifolium and Yucca. Furthermore, a new

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Galasso, Gabriele, Gianniantonio Domina, Nicola M.G. Ardenghi, et al. "Notulae to the Italian alien vascular flora: 3." Italian Botanist 3 (May 17, 2017): 49–71. https://doi.org/10.3897/italianbotanist.3.13126.

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In this contribution, new data concerning the Italian distribution of alien vascular flora are presented. It includes new records, exclusions, confirmations, and status changes for Italy or for Italian administrative regions for taxa in the genera Acer, Amaranthus, Araujia, Aubrieta, Avena, Bidens, Calycanthus, Celtis, Elaeagnus, Eragrostis, Euonymus, Fallopia, Ficus, Hedera, Lantana, Ligustrum, Ludwigia, Morus, Oenothera, Opuntia, Oxalis, Parkinsonia, Paspalum, Paulownia, Platycladus, Pleuropterus, Rumex, Salvia, Senecio, Setaria, Syagrus, Tradescantia, Trifolium and Yucca. Furthermore, a new

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27

Yuliarnis, Sri Kurnia, Yeka Hendriyani, Denny Kurniadi, and M. Giatman. "APPLICATION OF DATA MINING FOR ANALYSIS OF CONSUMER PURCHASE DATA ON SALES TRANSACTION DATA AT HALAL MART HNI HPAI DHARMASRAYA." JURNAL PENDIDIKAN TEKNOLOGI KEJURUAN 3, no. 1 (2020): 68–75. http://dx.doi.org/10.24036/jptk.v3i1.6923.

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The sales strategy determines the continuity of the business being run. The problems that occur are the sales archive data has not been analyzed in-depth, the information system has not been integrated with applications for sales data analysis, online media promotion has not been maximized, inadequate stock of goods, the layout of goods is not optimal, and the combination of the number of products is not optimal. This study aims to extract hidden information in the sales database using Data Mining. From the information generated, sales strategy recommendations are developed relating to promoti

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28

Zheng, Shuyu, Jehad Aldahdooh, Tolou Shadbahr, et al. "DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal." Nucleic Acids Research 49, W1 (2021): W174—W184. http://dx.doi.org/10.1093/nar/gkab438.

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Abstract Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehen

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29

Galasso, Gabriele, Gianniantonio Domina, Nicola Maria Giuseppe Ardenghi, et al. "Notulae to the Italian alien vascular flora: 2." Italian Botanist 2 (November 25, 2016): 55–71. https://doi.org/10.3897/italianbotanist.2.11144.

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In this contribution, new data concerning the Italian distribution of alien vascular flora are presented. It includes new records, exclusions and confirmations for Italy or for Italian administrative regions for taxa in the genera Ageratum, Aster, Buddleja, Cedrus, Centranthus, Cephalotaxus, Clerodendrum, Cotoneaster, Cyperus, Honorius, Lantana, Ligustrum, Morus, Muscari, Oenothera, Opuntia, Platycladus, Plumbago, Pseudotsuga, Sedum, Sporobolus, Stachys, Ulmus and Yucca. A nomen novum, Stachys talbotii, is proposed as a replacement name for Sideritis purpurea.

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30

Somayeh, Abdi, and Mohamadi Somayeh. "TWO LEVEL JOB SCHEDULING AND DATA REPLICATION IN DATA GRID." International Journal of Grid Computing & Applications (IJGCA) 1, no. 1 (2010): 1/15. https://doi.org/10.5121/ijgca.2010.1103.

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<strong>ABSTRACT</strong> Data Grid environment is a geographically distributed that deal with date-intensive application in scientific and enterprise computing. In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth, especially when hierarchy of bandwidth appears in network. Effective job scheduling can reduce data transfer time by considering hierarchy of bandwidth and also dispatching a job to where the needed data are present. Additionally, replication of data from primary repositories to other locations can be an impor

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31

Wolf, J. L., D. M. Dias, B. R. Iyer, and P. S. Yu. "Multisystem coupling by a combination of data sharing and data partitioning." IEEE Transactions on Software Engineering 15, no. 7 (1989): 854–60. http://dx.doi.org/10.1109/32.29485.

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32

Reen, Brian P., Huaqing Cai, Robert E. Dumais, Yuanfu Xie, Steve Albers, and John W. Raby. "Combining vLAPS and Nudging Data Assimilation." Atmosphere 13, no. 1 (2022): 127. http://dx.doi.org/10.3390/atmos13010127.

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The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out

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33

Anatoli, Nachev. "Data Mining Techniques for Analysing Employment Data." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (2019): 555–66. https://doi.org/10.35940/ijeat.B3311.129219.

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This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity ana

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34

Mansouri, Najme, Gholam Hosein Dastghaibyfard, and Ehsan Mansouri. "Combination of data replication and scheduling algorithm for improving data availability in Data Grids." Journal of Network and Computer Applications 36, no. 2 (2013): 711–22. http://dx.doi.org/10.1016/j.jnca.2012.12.021.

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35

Żurański, J. A., and A. Sobolewski. "An Analysis of Snow and Wind Loads Combinations Based on Meteorological Data." Archives of Civil Engineering 62, no. 4 (2016): 205–30. http://dx.doi.org/10.1515/ace-2015-0117.

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AbstractThe objective of this paper is to present a probabilistic method of analyzing the combinations of snow and wind loads using meteorological data and to determine their combination factors. Calculations are based on data measured at twelve Polish meteorological stations operated by the Institute for Meteorology and Water Management. Data provided are from the years 1966 - 2010. Five combinations of snow load and 10-minute mean wind velocity pressure have been considered. Gumbel probability distribution has been used to fit the empirical distributions of the data. As a result, the interde

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36

Jurczyk, Anna, Jan Szturc, Irena Otop, Katarzyna Ośródka, and Piotr Struzik. "Quality-Based Combination of Multi-Source Precipitation Data." Remote Sensing 12, no. 11 (2020): 1709. http://dx.doi.org/10.3390/rs12111709.

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A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data shou

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37

Fernández-Vázquez, Esteban, Blanca Moreno, and Geoffrey J. D. Hewings. "A Data-Weighted Prior Estimator for Forecast Combination." Entropy 21, no. 4 (2019): 429. http://dx.doi.org/10.3390/e21040429.

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Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good for

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38

Aljazaery, Ibtisam A., Haider Th Salim Alrikabi, and Mustafa Rabea Aziz. "Combination of Hiding and Encryption for Data Security." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 09 (2020): 34. http://dx.doi.org/10.3991/ijim.v14i09.14173.

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<pre>One of the techniques used in information security is the concealment technique, where the information to be hidden within another information medium to be saved in the process of messaging between two sides without detection. In this paper, an algorithm was proposed to conceal and encrypt data using several means.in order to ensure its preservation from detection and hackers. Wavelet transformer was used to change the shape of a wave of information (one and two-dimensional data) and its different mathematical formulas. Two sets of data were used, the first group used in a hidden pr

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Pugh, Trevor J., and Benjamin Haibe-Kains. "REFLECTions on Combination Therapies Empowered by Data Sharing." Cancer Discovery 12, no. 6 (2022): 1416–18. http://dx.doi.org/10.1158/2159-8290.cd-22-0330.

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Summary: Li and colleagues present REFLECT, a computational approach to precision oncology that nominates effective drug combinations by utilizing a diverse compendium of publicly available preclinical and clinical genomic, transcriptomic, and proteomic data. The preliminary validation of the REFLECT system in preclinical and clinical trial settings showcases potential for clinical implementation, although challenges remain. See related article by Li et al., p. 1542 (4).

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Samson, Kurt. "Encouraging Data for Immune Checkpoint Combination in RCC." Oncology Times 38 (November 2016): 11–12. http://dx.doi.org/10.1097/01.cot.0000508587.32690.0d.

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Campitelli, Marco, Nabil Zeineddine, Ghassan Samaha, and Stephen Maslak. "Combination Antifungal Therapy: A Review of Current Data." Journal of Clinical Medicine Research 9, no. 6 (2017): 451–56. http://dx.doi.org/10.14740/jocmr2992w.

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Haneuse, Sebastien J. P. A., and And Jonathan C. Wakefield. "The combination of ecological and case-control data." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, no. 1 (2008): 73–93. http://dx.doi.org/10.1111/j.1467-9868.2007.00628.x.

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Kirby, J. F. "Fast combination of satellite and marine gravity data." Exploration Geophysics 28, no. 1-2 (1997): 94–96. http://dx.doi.org/10.1071/eg997094.

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Porrill, John. "Optimal Combination and Constraints for Geometrical Sensor Data." International Journal of Robotics Research 7, no. 6 (1988): 66–77. http://dx.doi.org/10.1177/027836498800700606.

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Happ, Mary Beth, Annette DeVito Dabbs, Judith Tate, Allison Hricik, and Judith Erlen. "Exemplars of Mixed Methods Data Combination and Analysis." Nursing Research 55, Supplement 1 (2006): S43—S49. http://dx.doi.org/10.1097/00006199-200603001-00008.

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Wakefield, J., and S. Haneuse. "The Combination of Ecological and Case-Control Data." American Journal of Epidemiology 163, suppl_11 (2006): S170. http://dx.doi.org/10.1093/aje/163.suppl_11.s170-d.

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Kim, Ki Joong. "Combination of different data sets for phylogeny reconstruction." Korean Journal of Plant Taxonomy 26, no. 4 (1996): 293–327. http://dx.doi.org/10.11110/kjpt.1996.26.4.293.

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Parker, J. R. "Rank and response combination from confusion matrix data." Information Fusion 2, no. 2 (2001): 113–20. http://dx.doi.org/10.1016/s1566-2535(01)00030-6.

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Ng, Geok See, and Harcharan Singh. "Data equalisation with evidence combination for pattern recognition." Pattern Recognition Letters 19, no. 3-4 (1998): 227–35. http://dx.doi.org/10.1016/s0167-8655(98)00005-1.

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Komarova, Tatiana, Denis Nekipelov, and Evgeny Yakovlev. "Identification, data combination, and the risk of disclosure." Quantitative Economics 9, no. 1 (2018): 395–440. http://dx.doi.org/10.3982/qe568.

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