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Journal articles on the topic 'Data input'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Gadelha, Isaque Araujo. "Artificial Intelligence Security through Input Data Quality Steps." Núcleo do Conhecimento 02, no. 12 (2023): 24–38. https://doi.org/10.32749/nucleodoconhecimento.com.br/computer-science/input-data.

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In times of digital transformation, data generation constitutes a new type of asset, as it requires new forms of organizational learning, giving rise to new business models. The focus of all this transformation is Artificial Intelligence (AI), a subfield of Computer Science responsible for creating computational resources with capabilities similar to human reasoning for automated problem-solving. Elements of mathematics and engineering are used to reproduce aspects of human intelligence, as well as insights from other areas such as philosophy, mathematics, economics, neuroscience, psychology,

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Zhu, D., S. A. Billings, M. A. Balikhin, S. Wing, and H. Alleyne. "Multi-input data derivedDstmodel." Journal of Geophysical Research: Space Physics 112, A6 (2007): n/a. http://dx.doi.org/10.1029/2006ja012079.

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3

Felkey, Bill G., and Brent I. Fox. "Expanding Data Input Options." Hospital Pharmacy 39, no. 8 (2004): 802–8. http://dx.doi.org/10.1177/001857870403900812.

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4

Johnson, Mark E., and Mansooreh Mollaghasemi. "Simulation input data modeling." Annals of Operations Research 53, no. 1 (1994): 47–75. http://dx.doi.org/10.1007/bf02136826.

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5

Friebelová, Jana, and Ludvík Friebel. "Data envelopment analysis and negative input data." Acta Universitatis Bohemiae Meridionalis 10, no. 2 (2012): 85–88. http://dx.doi.org/10.32725/acta.2007.026.

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6

Arsenault, Kristi R., Sujay V. Kumar, James V. Geiger, et al. "The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems." Geoscientific Model Development 11, no. 9 (2018): 3605–21. http://dx.doi.org/10.5194/gmd-11-3605-2018.

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Abstract. The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assi

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Shiraz, Rashed Khanjani, Madjid Tavana, and Debora Di Caprio. "Chance-constrained data envelopment analysis modeling with random-rough data." RAIRO - Operations Research 52, no. 1 (2018): 259–84. http://dx.doi.org/10.1051/ro/2016076.

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Data envelopment analysis (DEA) is a useful management tool for measuring the relative efficiency of decision making units (DMUs) which consumes multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally indispensable in classical DEA models, real-world problems often involve random and/or rough input and output data. We present a chance-constrained DEA model with random and rough (random-rough) input and output data and propose a deterministic equivalent model with quadratic constraints to solve the model. The main contributions of this paper are fou

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8

Gunning-Schepers, Louise. "The input data for prevent." Health Policy 12, no. 1-2 (1989): 69–148. http://dx.doi.org/10.1016/0168-8510(89)90135-8.

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9

Cheng, Russell C. H., and Christine S. M. Currie. "Input modelling for multimodal data." Journal of the Operational Research Society 71, no. 6 (2019): 1038–52. http://dx.doi.org/10.1080/01605682.2019.1609887.

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10

PIRAMUTHU, S. "Input data for decision trees." Expert Systems with Applications 34, no. 2 (2008): 1220–26. http://dx.doi.org/10.1016/j.eswa.2006.12.030.

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Masayuk, S. "Data input/output control system." Expert Systems with Applications 9, no. 3 (1995): V. http://dx.doi.org/10.1016/0957-4174(95)98748-8.

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12

Kreuter, Daniel, Kheirollah Sepahvand, and Michael Beitelschmidt. "Homogenization with uncertain input data." PAMM 11, no. 1 (2011): 533–34. http://dx.doi.org/10.1002/pamm.201110256.

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13

Smith, Eric D., William T. Siefert, and David Drain. "Risk matrix input data biases." Systems Engineering 12, no. 4 (2009): 344–60. http://dx.doi.org/10.1002/sys.20126.

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14

Wu, Yaoxuan, Ahmad Humayun, Muhammad Ali Gulzar, and Miryung Kim. "Natural Symbolic Execution-Based Testing for Big Data Analytics." Proceedings of the ACM on Software Engineering 1, FSE (2024): 2677–700. http://dx.doi.org/10.1145/3660825.

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Symbolic execution is an automated test input generation technique that models individual program paths as logical constraints. However, the realism of concrete test inputs generated by SMT solvers often comes into question. Existing symbolic execution tools only seek arbitrary solutions for given path constraints. These constraints do not incorporate the naturalness of inputs that observe statistical distributions, range constraints, or preferred string constants. This results in unnatural-looking inputs that fail to emulate real-world data. In this paper, we extend symbolic execution with co

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15

KOZONO, Takahiro, and Yusuke FUJIMOTO. "Data-driven Feedforward Input Design for Optimal Servosystem with Input Constraint." Transactions of the Society of Instrument and Control Engineers 59, no. 3 (2023): 153–61. http://dx.doi.org/10.9746/sicetr.59.153.

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16

Sivasubramaniam, K., K. Alfredsen, T. Rinde, and B. Sæther. "Can model-based data products replace gauge data as input to the hydrological model?" Hydrology Research 51, no. 2 (2020): 188–201. http://dx.doi.org/10.2166/nh.2020.076.

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Abstract Hydrological models require accurate and representative meteorological inputs for better prediction of discharge and hence, the efficient management of water resources. Numerical weather prediction model-based reanalysis data products on the catchment scale are becoming available, and they could be an alternative input data for hydrological models. This study focuses on the applicability of a set of model-based data as input to hydrological models used in inflow predictions for operational hydropower production planning of three hydropower systems in middle Norway. First, the study co

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Lindblom, E., U. Jeppsson, and G. Sin. "Identification of behavioural model input data sets for WWTP uncertainty analysis." Water Science and Technology 81, no. 8 (2019): 1558–68. http://dx.doi.org/10.2166/wst.2019.427.

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Abstract Uncertainty analysis is important for wastewater treatment plant (WWTP) model applications. An important aspect of uncertainty analysis is the identification and proper quantification of sources of uncertainty. In this contribution, a methodology to identify an ensemble of behavioural model representations (combinations of input data, model structure and parameter values) is presented and evaluated. The outcome is a multivariate conditional distribution of input data that is used for generating samples of likely inputs (such as Monte Carlo input samples) to perform WWTP model uncertai

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18

Jahanshahloo, G. R., F. Hosseinzadeh Lotfi, M. Rostamy-Malkhalifeh, and S. Ghobadi. "Using Enhanced Russell Model to Solve Inverse Data Envelopment Analysis Problems." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/571896.

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This paper studies the inverse data envelopment analysis using the nonradial enhanced Russell model. Necessary and sufficient conditions for inputs/outputs determination are introduced based on Pareto solutions of multiple-objective linear programming. In addition, an approach is investigated to identify extra input/lack output in each of input/output components (maximum/minimum reduction/increase amounts in each a of input/output components). In addition, the following question is addressed: if among a group of DMUs, it is required to increase inputs and outputs to a particular unit and assum

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19

Xia, Meimei. "Choquet-Integral-Based Data Envelopment Analysis with Stochastic Multicriteria Acceptability Analysis." Symmetry 14, no. 4 (2022): 642. http://dx.doi.org/10.3390/sym14040642.

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Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiencies of decision-making units (DMUs) by using a set of inputs and a set of outputs. However, traditional DEA models always assume that the inputs or outputs are independent of each other, which is unrealistic in practical problems. To reflect the interactions between inputs or outputs, the Choquet integral is employed in DEA models. The traditional DEA models are usually used to find some specific input and output weights of DMUs to optimize the efficiency score of DMUs, but the corresponding input and output

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20

Mönks, Uwe, Helene Dörksen, Volker Lohweg, and Michael Hübner. "Information Fusion of Conflicting Input Data." Sensors 16, no. 11 (2016): 1798. http://dx.doi.org/10.3390/s16111798.

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21

Rossides, Michael T. "Voice input system for data retrieval." Journal of the Acoustical Society of America 99, no. 6 (1996): 3284. http://dx.doi.org/10.1121/1.414982.

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22

Yilmaz, Anil, and Ihsan Sabuncuoglu. "Input Data Analysis Using Neural Networks." SIMULATION 74, no. 3 (2000): 128–37. http://dx.doi.org/10.1177/003754970007400301.

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23

Hendtlass, Tim. "Input Data Analysis by Neural Network." Journal of Computational and Theoretical Nanoscience 7, no. 5 (2010): 862–67. http://dx.doi.org/10.1166/jctn.2010.1432.

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24

Koupal, John, Timothy DeFries, Cindy Palacios, Scott Fincher, and Diane Preusse. "Motor Vehicle Emissions Simulator Input Data." Transportation Research Record: Journal of the Transportation Research Board 2427, no. 1 (2014): 63–72. http://dx.doi.org/10.3141/2427-07.

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25

Greis, Miriam, Hendrik Schuff, Marius Kleiner, Niels Henze, and Albrecht Schmidt. "Input Controls for Entering Uncertain Data." Proceedings of the ACM on Human-Computer Interaction 1, EICS (2017): 1–17. http://dx.doi.org/10.1145/3095805.

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26

Panulinová, Eva. "Input Data for Tram Noise Analysis." Procedia Engineering 190 (2017): 371–76. http://dx.doi.org/10.1016/j.proeng.2017.05.351.

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27

Kyselova, A. H., G. D. Kiselov, A. A. Serhyeyev, and A. V. Shalaginov. "Processing input data in multimodal applications." Electronics and Communications 16, no. 2 (2011): 86–92. http://dx.doi.org/10.20535/2312-1807.2011.16.2.268253.

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28

Rai, Amit, and Kazuki Saito. "Omics data input for metabolic modeling." Current Opinion in Biotechnology 37 (February 2016): 127–34. http://dx.doi.org/10.1016/j.copbio.2015.10.010.

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29

Boynton, R. J., M. A. Balikhin, S. A. Billings, A. S. Sharma, and O. A. Amariutei. "Data derived NARMAX Dst model." Annales Geophysicae 29, no. 6 (2011): 965–71. http://dx.doi.org/10.5194/angeo-29-965-2011.

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Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar wind-magnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p1/2V4/3BTsin6(θ/2), has been implemented as an input to model the Dst

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30

Munaf, Alfatihah Reno MNSP, Amanda Pratama Putra, Wa Ode Zuhayeni Madjida, Ignatius Aditya Setyadi, and Amin Rois Sinung Nugroho. "Data Input Quality Metrics on Mobile Positioning Data (MPD)." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (2022): 833–41. http://dx.doi.org/10.34123/icdsos.v2021i1.134.

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Statistics Indonesia (BPS) has been using Mobile Positioning Data (MPD) to support official statistics since 2016. As a source of big data, MPD also has veracity characteristics, indicating uncertainty in the data. Therefore, it is necessary to check that the data are good enough to allow further analysis and the quality assurance process. Currently, there is no established international standard for quality assurance of MPD. This paper describes the quality matrix used by BPS in examining data from mobile operators. BPS uses thirteen indicators in conducting quality assurance, where the inspe

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31

Pan, Xiao Heng, Yang Ping Li, and Hua Qiang Yuan. "Reformulating Input Data for Linear Regression of Demographic Data." Advanced Materials Research 910 (March 2014): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amr.910.401.

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In regressing demographic data, neighborhood information needs to be taken into account, which makes conventional linear regression models inappropriate. In this paper, to incorporate neighborhood information without incurring extra computational cost, we investigate various possibilities to reformulate input data. Experiments show that augmenting each site with its neighborhood information gives the best results.

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32

Irsan, Robby, and Qolby Istiqomah. "Analisis Data Meteorologi Untuk Input Data Software AERMOD VIEW." Jurnal Teknologi Lingkungan Lahan Basah 11, no. 2 (2023): 579. http://dx.doi.org/10.26418/jtllb.v11i2.67238.

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Suatu keadaan dimana kualitas udara menurun dan terkontaminasi zat, dan apabila zat tersebut berbahaya maupun tidak berbahaya yang menyebebkan gangguan kesehatan manusia disebut pencemaran udara. Salah satu jenis pencemar yang sangat perlu diperhatikan yaitu pencemaran udara akibat pengoperasan mesin seperti Pembangkit Listrik Tenaga Uap (PLTU). PT. X merupakan perusahaan yang menggunakan pembangkit listrik tenaga uap berbahan bakar batu bara dan solar untuk pengoperasian pabrik. Akibat pengoperasian PLTU menghasilkan beberapa gas buang seperti, Nitrogen Dioksida (NO2), Sulfur Dioksida (SO2),

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33

Plaga, Leonie Sara, and Valentin Bertsch. "cd2es: Converting climate data to energy system input data." Journal of Open Source Software 10, no. 108 (2025): 7541. https://doi.org/10.21105/joss.07541.

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34

Koesdijati, Titiek, and Andarmadi Jati Abdhi Wasesa. "Efficiency Distribution Analysis with Data Envelopment Analysis (DEA) Approach Fertilizer." Tibuana 4, no. 02 (2021): 91–98. http://dx.doi.org/10.36456/tibuana.4.02.4010.91-98.

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Distribution channels have an important meaning for achieving company success in the field of marketing so that company management is required to always be responsive and able to adapt to environmental changes. Input and output data processing is done by giving weights to the input and output using the DEA CCR primal model by maximizing the input-oriented-based objective function. The results of processing the efficiency scale show the relative efficiency level of the scale of each DMU in the company. The efficiency scale is obtained through the formulation of the DEA CCR primal model between

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SUZUKI, Masayasu, and Mitsuo HIRATA. "Multi-input Exact Linearization Method for PWM-type Input Sampled-data Systems." Transactions of the Society of Instrument and Control Engineers 54, no. 2 (2018): 194–200. http://dx.doi.org/10.9746/sicetr.54.194.

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36

Fadavi, R., A. Keyhani, and S. S. Mohtasebi. "An analysis of energy use, input costs and relation between energy inputs and yield of apple orchard." Research in Agricultural Engineering 57, No. 3 (2011): 88–96. http://dx.doi.org/10.17221/0/2010-rae.

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This study examines the energy balance between the input and the output per hectare for an apple orchard in the West Azarbaijan province in Iran (2008&ndash;2009). Data were collected by using random sampling method for 80 &ldquo;face to face&rdquo; questioners. Results showed that the highest share of energy consumption belongs to packaging (57%) and irrigation (16%). The highest share of expenses was found to be 34% and 30% for labor and packaging, respectively. The total energy input for apple production, energy productivity, net energy and output-input energy value were estimat

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37

Ren, Tao, Qiang Zhang, Meng Qian Tang, and Yan Fei Yang. "Synchronization Controller Design for Chaotic System with Input Sampled-Data Missing." Applied Mechanics and Materials 716-717 (December 2014): 1572–77. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.1572.

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For the chaotic systems with sampled data input, the discontinuous nonlinear system is transformed into a discrete system, and the switched system is proposed considering the control inputs missing. By using the zero input method, a sufficient condition is derived for the exponential stability of the error system. Simulation results illustrate the effectiveness of the proposed sampled-data control method.

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Lence, Sergio H., and Douglas J. Miller. "Recovering Output‐Specific Inputs from Aggregate Input Data: A Generalized Cross‐Entropy Approach." American Journal of Agricultural Economics 80, no. 4 (1998): 852–67. http://dx.doi.org/10.2307/1244069.

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39

Golpîra, Hêriş. "A Hybrid Clustering Method Using Balanced Scorecard and Data Envelopment Analysis." International Journal Of Innovation And Economic Development 1, no. 7 (2015): 15–25. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.17.2002.

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This paper introduces a new hybrid clustering method using Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methods. DEA cannot identify its’ input and output itself, and it is a major weakness of the DEA. In the proposed method, this gap is resolved by integrating DEA with BSC. Some decision-making units (DMUs) needed in DEA method, in compliance with some inputs and outputs is the major drawback of this integration. To deal with this disadvantage, the proposed method selects the most important strategic factors, attained from the BSC method. These data considered to be the input

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40

Romana, Bangash. "Performance Evaluation of Mutual Funds: A Data Envelopment Analysis." Global Social Sciences Review 3, no. 2 (2018): 212–36. https://doi.org/10.5281/zenodo.4331472.

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This study conducts a regression analysis between the efficiency scores and the explanatory variables. Data was collected for explanatory variables like age of the mutual fund, size of fund family, number of funds in funds family, and volatility (beta). As this study used input oriented model, mutual funds were categorized and relatively evaluated on the basis of similar outcomes and inputs charged. Out of 44 mutual funds understudy, only 7 of the mutual funds were cost efficient. This indicates that nearly 37 of the mutual funds under study have more costs associated to them as compared to th

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Romana, Bangash, Hussain Arif, and Hassan Azhar Muhammad. "Performance Evaluation of Mutual Funds: A Data Envelopment Analysis." GLOBAL SOCIAL SCIENCES REVIEW (GSSR) III, no. II (2018): 212–36. https://doi.org/10.31703/gssr.2018(III-II).14.

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This study conducts a regression analysis between the efficiency scores and the explanatory variables. Data was collected for explanatory variables like age of the mutual fund, size of fund family, number of funds in funds family, and volatility (beta). As this study used input oriented model, mutual funds were categorized and relatively evaluated on the basis of similar outcomes and inputs charged. Out of 44 mutual funds understudy, only 7 of the mutual funds were cost efficient. This indicates that nearly 37 of the mutual funds under study have more costs associated to them as compared to th

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42

Baardsen, Sjur. "An Econometric Analysis of Norwegian Sawmilling 1974–1991 Based on Mill-Level Data." Forest Science 46, no. 4 (2000): 537–47. http://dx.doi.org/10.1093/forestscience/46.4.537.

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Abstract An eight-input translog total cost function was estimated, and the estimates were used to calculate Allen and Morishima substitution elasticities, price elasticities of conditional input demands, elasticity of size, and technical change for Norwegian sawmilling at a five-digit disaggregate industry level, based on an unbalanced panel of 3,090 individual observations of sawmills over the period 1974–1991. The unusually rich source of data made it possible to analyze the production economics of sawmilling in more detail than has ever been possible before. As far as comparisons with othe

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43

Hanafusa, Ryo, and Takeshi Okadome. "Bayesian Kernel Regression for Noisy Inputs Based on Nadaraya–Watson Estimator Constructed from Noiseless Training Data." Advances in Data Science and Adaptive Analysis 12, no. 01 (2020): 2050004. http://dx.doi.org/10.1142/s2424922x20500047.

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In regression for noisy inputs, noise is typically removed from a given noisy input if possible, and then the resulting noise-free input is provided to the regression function. In some cases, however, there is no available time or method for removing noise. The regression method proposed in this paper determines a regression function for noisy inputs using the estimated posterior of their noise-free constituents with a nonparametric estimator for noiseless explanatory values, which is constructed from noiseless training data. In addition, a probabilistic generative model is presented for estim

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Ulkhaq, M. Mujiya. "Technical Efficiency of Indonesian Sharia Banks: A Data Envelopment Analysis Approach." Al-Muhasib: Journal of Islamic Accounting and Finance 3, no. 1 (2023): 86–99. http://dx.doi.org/10.30762/almuhasib.v3i1.242.

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The existence of competition among sharia banks is one of the effects that occur with the development of sharia banks. The purpose of this study is to analyze the efficiency of Sharia banks in Indonesia. Understanding efficiency is important because if banks are always operating at full efficiency, the fewer inputs they use, the greater the output they achieve. Output is measured by total earning assets, while the inputs used are total deposits and total operating expenses. A data envelopment analysis with input-oriented and variable returns to scale is used to achieve the objective. The resul

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Guo, Hongyu. "Nonlinear Mixup: Out-Of-Manifold Data Augmentation for Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4044–51. http://dx.doi.org/10.1609/aaai.v34i04.5822.

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Data augmentation with Mixup (Zhang et al. 2018) has shown to be an effective model regularizer for current art deep classification networks. It generates out-of-manifold samples through linearly interpolating inputs and their corresponding labels of random sample pairs. Despite its great successes, Mixup requires convex combination of the inputs as well as the modeling targets of a sample pair, thus significantly limits the space of its synthetic samples and consequently its regularization effect. To cope with this limitation, we propose “nonlinear Mixup”. Unlike Mixup where the input and lab

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Mai, Derong, Rod E. Turochy, and David H. Timm. "Correlation-Based Clustering of Traffic Data for the Mechanistic–Empirical Pavement Design Guide." Transportation Research Record: Journal of the Transportation Research Board 2339, no. 1 (2013): 104–11. http://dx.doi.org/10.3141/2339-12.

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Development of traffic data clusters is crucial for use of the Mechanistic–Empirical Pavement Design Guide (MEPDG) when site-specific traffic data are not available and statewide data are too general. However, a preferred approach to traffic data clustering is not specified in the MEPDG. In current clustering practice, subjective decisions are made about issues such as determination of the number of clusters. This paper presents a new clustering combination method, correlation-based clustering, that considers the effects of traffic inputs on pavement design thicknesses, so that determination o

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47

Gross, Günter. "On the range of boundary layer model results depending on inaccurate input data." Meteorologische Zeitschrift 28, no. 3 (2019): 225–34. http://dx.doi.org/10.1127/metz/2019/0952.

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48

Kim, Jinwook, Kyungroul Lee, and Hanjo Jeong. "Real-Time Mouse Data Protection Method Using GANs for Image-Based User Authentication Based on GetCursorPos() and SetCursorPos() Functions." Applied Sciences 15, no. 2 (2025): 977. https://doi.org/10.3390/app15020977.

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In online services, password-based authentication, a prevalent method for user verification, is inherently vulnerable to keyboard input data attacks. To mitigate these vulnerabilities, image-based authentication methods have been introduced. However, these approaches also face significant security challenges due to the potential exposure of mouse input data. To address these threats, a protective technique that leverages the SetCursorPos() function to generate artificial mouse input data has been developed, thereby concealing genuine user inputs. Nevertheless, adversaries employing advanced ma

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Do, Nam H., Tien Van Do, Lóránt Farkas, and Csaba Rotter. "Provisioning Input and Output Data Rates in Data Processing Frameworks." Journal of Grid Computing 18, no. 3 (2020): 491–506. http://dx.doi.org/10.1007/s10723-020-09508-0.

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Abstract This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.

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Udina, A., and Lc Jain. "Skylight Light Intensity Data Logging System." Active and Passive Electronic Components 20, no. 1 (1997): 7–17. http://dx.doi.org/10.1155/1997/61913.

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The design of a skylight intensity data logging system is presented. The proposed system is able to accept 32 inputs in two 16 input blocks with capabilities for further expansion. A typical application would be to simultaneously monitor real time change in light intensity in two skylight units, over a chosen periods of minutes, hours or days.

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