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Journal articles on the topic 'Area under the error curve'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Fui, Liau Heng, and Dino Isa. "Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve." International Journal of Applied Evolutionary Computation 2, no. 1 (2011): 18–33. http://dx.doi.org/10.4018/jaec.2011010102.

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Feature selection is crucial to select an “optimized” subset of features from the original feature set based on a certain objective function. In general, feature selection removes redundant or irrelevant data while retaining classification accuracy. This paper proposes a feature selection algorithm that aims to minimize the area under the curve of detection error trade-off (DET) curve. Particle swarm optimization (PSO) is employed to search for the optimal feature subset. The proposed method is implemented in face recognition and iris recognition systems. The result shows that the proposed met

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Chen, Li-Pang. "Analysis of Receiver Operating Characteristic Curves for Cure Survival Data and Mismeasured Biomarkers." Mathematics 13, no. 3 (2025): 424. https://doi.org/10.3390/math13030424.

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Cure models and receiver operating characteristic (ROC) curve estimation are two important issues in survival analysis and have received attention for many years. In the development of biostatistics, these two topics have been well discussed separately. However, a rare development in the estimation of the ROC curve has been made available based on survival data with the cure fraction. On the other hand, while a large body of estimation methods have been proposed, they rely on an implicit assumption that the variables are precisely measured. In applications, measurement errors are generally ubi

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Lum, Bert L., Kevin J. Lane, Timothy W. Synold, Adrian Goram, Steven B. Charnick, and Branimir I. Sikic. "Validation of a Limited Sampling Model to Determine Etoposide Area Under the Curve." Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy 17, no. 5 (1997): 887–90. http://dx.doi.org/10.1002/j.1875-9114.1997.tb03779.x.

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Study Objective. To validate the utility of a previously reported 3‐point limited sampling model (LSM) for determining etoposide area under the curve to infinity (AUC∞).Design. Secondary analysis of data from two clinical trials of etoposide.Setting. University medical center clinical research center.Patients. Thirty‐four patients with different malignancies.Interventions. Etoposide was administered as a 2‐hour infusion to 34 patients. Serial plasma samples were drawn over 24 hours after the infusion and analyzed for etoposide by high‐performance liquid chromatography.Measurements and Main Res

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Grevel, J., and B. D. Kahan. "Abbreviated kinetic profiles in area-under-the-curve monitoring of cyclosporine therapy." Clinical Chemistry 37, no. 11 (1991): 1905–8. http://dx.doi.org/10.1093/clinchem/37.11.1905.

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Abstract Abbreviated kinetic profiles can reduce the number of phlebotomies and drug assays, and thereby the cost of area-under-the-curve (AUC) monitoring. In the present investigation, we used two independent data sets: group 1, 101 AUC profiles from 77 stable renal-transplant patients, which included a 5-h sample in addition to the usual 0-, 2-, 4-, 6-, 10-, 14-, and 24-h samples; and group 2, 100 profiles from 50 stable renal-transplant patients before and after a change in their daily oral dose of cyclosporine. Group I demonstrated a fair correlation between cyclosporine trough concentrati

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Xu, Xinfeng, Jian Shi, and Yuanjing Zhao. "Experimental Study on Plastic Deformation of EH690 Steel with the V-Notch during Falling Weight Impact Based on DIC." Processes 12, no. 4 (2024): 751. http://dx.doi.org/10.3390/pr12040751.

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In this study, a falling weight impact test was conducted on EH690 steel specimens with V-notches using Digital Imaging Correlation (DIC). In conjunction with scanning electron microscopy (SEM), the plastic deformation and crack initiation processes were examined at the notch of the specimen under different impact energies (90 J, 120 J, 135 J and 150 J). ABAQUS was used to simulate the plastic deformation of an EH690 specimen. The results show that the strain at the notch tip experienced some elasticity and yielding as the load increased under different impact energies. The load remains unchan

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Murdoch, Andrew R., Chad J. Herring, Charles H. Frady, Kevin See, and Chris E. Jordan. "Estimating observer error and steelhead redd abundance using a modified Gaussian area-under-the-curve framework." Canadian Journal of Fisheries and Aquatic Sciences 75, no. 12 (2018): 2149–58. http://dx.doi.org/10.1139/cjfas-2017-0335.

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This study examined how a suite of habitat and environmental variables relate to the ability of a stream surveyor to identify (observer efficiency) and distinguish (observer accuracy) steelhead (Oncorhynchus mykiss) redds from other stream features. Two existing spawning survey protocols that included one or two redd observers were used to develop models to estimate redd observer error. In most cases, steelhead redd abundances using raw redd counts were underestimated. Mean annual rates of observer efficiency ranged from 0.44 to 0.57, and observer accuracy ranged from 0.67 to 0.83. Regardless

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Bandos, Andriy I., Ben Guo, and David Gur. "Jackknife variance of the partial area under the empirical receiver operating characteristic curve." Statistical Methods in Medical Research 26, no. 2 (2014): 528–41. http://dx.doi.org/10.1177/0962280214551190.

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Receiver operating characteristic analysis provides an important methodology for assessing traditional (e.g., imaging technologies and clinical practices) and new (e.g., genomic studies, biomarker development) diagnostic problems. The area under the clinically/practically relevant part of the receiver operating characteristic curve (partial area or partial area under the receiver operating characteristic curve) is an important performance index summarizing diagnostic accuracy at multiple operating points (decision thresholds) that are relevant to actual clinical practice. A robust estimate of

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She, Yuxuan, Jiahao Cui, and Xinran Liu. "Comparison of methods for calculating confidence intervals of AUC in ROC curve considering sampling error." Applied and Computational Engineering 46, no. 1 (2024): 189–98. http://dx.doi.org/10.54254/2755-2721/46/20241317.

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The Receiver Operating Characteristic (ROC) curve is a crucial method for evaluating the effectiveness of diagnostic medical indicators and has found extensive applications. However, errors are inevitable in the data acquisition process. Therefore, discussions on error and various methods for improving and handling data have not only become the focus of academic discourse but also hold practical significance. Unlike general statistics, the diversity of error situations, ranges, and impacts in biostatistics often present unique challenges. In practical scenarios, such as drug experiments, limit

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Xu, Jingjing, Xu Xu, and Wenjuan Yao. "New Method for Calculating the Settlement of Single Pile and Pile Group in Soft Soil Area." Advances in Civil Engineering 2020 (November 10, 2020): 1–9. http://dx.doi.org/10.1155/2020/8816704.

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This paper fits the τ-z curve of a single pile by mathematical methods. Based on the one-dimensional similarity of the τ-z curves, the τ-z curves of single pile under different loads are derived. It studies the distribution of the axial force of a single pile by taking the self-weight of the pile and the pile-end resistance ratio into account and establishes the calculated equation of settlement for single pile. The mutual reinforcing effect between the piles is fully considered, and the settlement of each foundation pile in the pile group is deduced in use of shear displacement method. The ex

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Gaspari, F., M. F. Anedda, O. Signorini, G. Remuzzi, and N. Perico. "Prediction of cyclosporine area under the curve using a three-point sampling strategy after Neoral administration." Journal of the American Society of Nephrology 8, no. 4 (1997): 647–52. http://dx.doi.org/10.1681/asn.v84647.

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Accurate monitoring of cyclosporine (CsA) dosage is still a problem, because measurement of the area under the curve (AUC)--the most appropriate indicator of exposure to CsA--requires a number of blood samples to be taken over 12 h, which makes monitoring difficult in day-to-day clinical practice. This study investigated whether a limited sampling strategy in human kidney transplantation reflected the actual AUC better in patients given Neoral than in those being treated with Sandimmune. Stepwise multiple regression analysis of CsA blood levels recorded after Neoral administration to 20 renal

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Takenouchi, Takashi, Osamu Komori, and Shinto Eguchi. "An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification." Neural Computation 24, no. 10 (2012): 2789–824. http://dx.doi.org/10.1162/neco_a_00336.

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While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimu

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Tuah, Ainul Nadira, Abu Bakar Ibrahim, Sumayyah Dzulkifly, Fauzi Mohammad Yusof, Ramli Awang Nor, and Razali Ariffin. "Analysis of the Area Under a Curve (AUC) using C-programming: Trapezium and Simpson rules techniques." Journal of ICT in Education 9, no. 1 (2022): 143–53. http://dx.doi.org/10.37134/jictie.vol9.1.12.2022.

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This study aimed to analyze the Area Under a Curve (AUC) using C-programming i.e. Trapezium and Simpson rules. There are various mathematical methods that can be applied to solve numerical integration for different data spaces. Among them are the Trapezium and Simpson rules which are widely used to solve numerical integration equations. The objective of this study was to study the calculation of the area under the curve more accurately and to identify the error differences between the Trapezium and Simpson rules in the calculation of the area under the curve. In addition, it also compares the

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Tang, Jinyuan, Zehua Hu, Siyu Chen, and Duncai Lei. "Effects of directional rotation radius and transmission error on the dynamic characteristics of face gear transmission system." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 7 (2013): 1108–18. http://dx.doi.org/10.1177/0954406213500745.

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The effects of directional rotation radius and transmission error excitation on the nonlinear dynamic characteristics of face gear transmission system are analyzed. First, the accurate time-varying mesh stiffness is calculated using finite element method, and the nonlinear motion equation of the system under static transmission error excitation is proposed. The frequency response curve, time history curve, dynamic mesh force curve and dynamic factor curve are given, and the phenomena of jump, multiple solutions and tooth impact are observed. The numerical results show that the effect of amplit

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Narasimhan, Harikrishna, and Shivani Agarwal. "Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve." Neural Computation 29, no. 7 (2017): 1919–63. http://dx.doi.org/10.1162/neco_a_00972.

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The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the

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Sung, MinDong, Sangchul Hahn, Chang Hoon Han, et al. "Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study." JMIR Medical Informatics 9, no. 11 (2021): e26426. http://dx.doi.org/10.2196/26426.

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Background In the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record–based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors. Objective In this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input. Methods A total of 21,738 patients were included in the development cohort. Three events—death, sepsis, and

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Li, Yanhong, John J. Koval, Allan Donner, and G. Y. Zou. "Interval estimation for the area under the receiver operating characteristic curve when data are subject to error." Statistics in Medicine 29, no. 24 (2010): 2521–31. http://dx.doi.org/10.1002/sim.4015.

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Shi, Jian, Kepeng Chen, and Xunhu Wei. "Research on Error Correction Model of Transformer-Meter Relationship in Low Voltage Distribution Network Based on Combined Clustering Strategy." Journal of Physics: Conference Series 2337, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2337/1/012010.

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Abstract At a time when distributed microgrids are gradually becoming the main form of renewable energy access, the distribution network plays an important role in serving the access and consumption of renewable energy, and is a powerful guarantee to promote the realization of the “double carbon” goal. However, due to the complex structure, frequent file changes, untimely governance and maintenance, the distribution station area cannot accurately perceive the ownership of transformer-meter relationship files. This paper presents an error correction method of transformer-meter relationship file

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Chen, Yongliang, Wei Wu, and Qingying Zhao. "A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping." Minerals 9, no. 5 (2019): 317. http://dx.doi.org/10.3390/min9050317.

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One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat

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Liu, Yangguang, Yangming Zhou, Shiting Wen, and Chaogang Tang. "A Strategy on Selecting Performance Metrics for Classifier Evaluation." International Journal of Mobile Computing and Multimedia Communications 6, no. 4 (2014): 20–35. http://dx.doi.org/10.4018/ijmcmc.2014100102.

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The evaluation of classifiers' performances plays a critical role in construction and selection of classification model. Although many performance metrics have been proposed in machine learning community, no general guidelines are available among practitioners regarding which metric to be selected for evaluating a classifier's performance. In this paper, we attempt to provide practitioners with a strategy on selecting performance metrics for classifier evaluation. Firstly, the authors investigate seven widely used performance metrics, namely classification accuracy, F-measure, kappa statistic,

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Schisterman, E. F. "Statistical Inference for the Area under the Receiver Operating Characteristic Curve in the Presence of Random Measurement Error." American Journal of Epidemiology 154, no. 2 (2001): 174–79. http://dx.doi.org/10.1093/aje/154.2.174.

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Shi, Yong-Guo, and Zhihua Wang. "Topological Conjugacy Between Skew Tent Maps." International Journal of Bifurcation and Chaos 25, no. 09 (2015): 1550118. http://dx.doi.org/10.1142/s0218127415501187.

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This paper investigates the conjugacy of any two skew tent maps. An explicit formula is given for the conjugacy. It is proved that the conjugacy is singular, Hölder continuous and not differentiable as well as its inverse. We calculate the arc-length of the conjugacy curve and the area under the conjugacy curve. We construct a sequence of functions to approximate the conjugacy, and give an estimation for the error of the approximation.

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Zhang, Tao, and Yongming Ren. "Identification and detection of a void under highway cement concrete pavement slabs based on finite element analysis." Rudarsko-geološko-naftni zbornik 34, no. 3 (2019): 41–47. http://dx.doi.org/10.17794/rgn.2019.3.5.

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For a city, the construction of a road is related to the development of economy, and the construction of a road includes maintenance of the road. Cement roads have been popularized in road construction due to their low construction cost and high strength, but at the same time, road problems have arisen. In order to solve and prevent the problem of voids under highway cement concrete pavement slabs, the acoustic vibration method was used for the detection of voids under highway cement concrete pavement slabs. This method was brieﬂy introduced and analysed with ﬁnite element analysis. Then, exci

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Han, Dongxu, Nan Zhou, and Zeyu Chen. "Enhancing State of Health Prediction Accuracy in Lithium-Ion Batteries through a Simplified Health Indicator Method." Batteries 10, no. 10 (2024): 342. http://dx.doi.org/10.3390/batteries10100342.

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Accurately predicting the state of health (SOH) of lithium-ion batteries is crucial for optimizing battery performance and achieving efficient energy management, especially in electric vehicle applications. However, the existing incremental capacity analysis methods, which are mostly based on curve multi-parameter analysis, still have limitations in terms of computation, prediction accuracy, and adaptability to actual operating conditions. This paper conducts an in-depth analysis of the incremental capacity (IC) curve and proposes a feature parameter based on the area under the IC curve. By in

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King, Christopher Ryan, Joanna Abraham, Bradley A. Fritz, et al. "Predicting self-intercepted medication ordering errors using machine learning." PLOS ONE 16, no. 7 (2021): e0254358. http://dx.doi.org/10.1371/journal.pone.0254358.

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Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that an

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Fang, Lei, Christopher L. Muhlstein, Amber L. Romasco, James G. Collins, and Lawrence H. Friedman. "Augmented instrumented indentation using nonlinear electrical contact current-voltage curves." Journal of Materials Research 24, no. 5 (2009): 1820–32. http://dx.doi.org/10.1557/jmr.2009.0213.

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An electrical technique was recently developed to measure the in situ contact area continuously during instrumented indentation by simultaneously monitoring electrical contact response between a conductive indenter tip and a conductive sample. This technique has the potential to overcome limitations of the Oliver-Pharr method caused by the lack of a direct contact area measurement. However, the electrical contact current-voltage (I-V) curves measured from the technique were nonlinear, posing a significant challenge to inferring accurate in situ contact areas. To overcome this challenge and ext

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Sun, Wen Bin. "Dynamic Response Analysis and Optimal Design of a RC Slab to Blast Loads." Advanced Materials Research 163-167 (December 2010): 2390–96. http://dx.doi.org/10.4028/www.scientific.net/amr.163-167.2390.

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Current guidelines such as TM5 and ASCE use a trial and error procedure to design RC slabs against blast loads. Although the trial and error procedure is easy to implement, it may not result in a optimal to resist blast loads. In this study, SDOF system recommended by TM5 and ASCE was adopted to simplify RC slabs; the bilinear model was selected to simulate the resistance-deflection curve for dynamic response analysis. After comparing the areas under the resistance-deflection curves of RC slabs with different reinforcement ratios, the reinforcement ratio responding to the biggest area can be d

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Millar, Russell B., and Chris E. Jordan. "A simple variance estimator for the trapezoidal area-under-the-curve estimator of the spawner abundance of Pacific salmon." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 8 (2013): 1231–39. http://dx.doi.org/10.1139/cjfas-2012-0499.

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The trapezoidal area-under-the-curve (TAUC) method is the simplest and most widely used method for estimating the spawning abundance of Pacific salmon from periodic surveys of spawner counts within a surveyed area. However, there is currently no method to estimate the precision of the estimated spawner abundance from the survey data. For this reason the Gaussian area-under-the-curve (GAUC) method was recently presented as an easily implemented alternative that has the advantage of having a variance estimator. However, under a diverse variety of simulation scenarios, the TAUC estimator was seen

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Corny, Jennifer, Asok Rajkumar, Olivier Martin, et al. "A machine learning–based clinical decision support system to identify prescriptions with a high risk of medication error." Journal of the American Medical Informatics Association 27, no. 11 (2020): 1688–94. http://dx.doi.org/10.1093/jamia/ocaa154.

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Abstract Objective To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks. Materials and Methods Data from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient’s set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2

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Lambert, Jerome, Philippe Halfon, Guillaume Penaranda, Pierre Bedossa, Patrice Cacoub, and Fabrice Carrat. "How to Measure the Diagnostic Accuracy of Noninvasive Liver Fibrosis Indices: The Area Under the ROC Curve Revisited." Clinical Chemistry 54, no. 8 (2008): 1372–78. http://dx.doi.org/10.1373/clinchem.2007.097923.

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Abstract Background: The area under the ROC curve (AUC) is widely used to measure the diagnostic accuracy of noninvasive fibrosis indices. However, use of the AUC assumes a binary gold standard, whereas fibrosis staging is based on an ordinal scale and also depends on the distribution of fibrosis stages in the study sample. We explored other fibrosis staging accuracy measures designed for ordinal gold standards, the C-statistic and the Obuchowski measure. Methods: We performed a simulation study to assess the bias in estimating the accuracy measures when the distribution of fibrosis stages in

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Cook, Nancy R., Bernard A. Rosner, Wei Chen, Sathanur R. Srinivasan, and Gerald S. Berenson. "Using the area under the curve to reduce measurement error in predicting young adult blood pressure from childhood measures." Statistics in Medicine 23, no. 22 (2004): 3421–35. http://dx.doi.org/10.1002/sim.1921.

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Galindo-Castillo, Ronaldo, Santos Gabriel Campos-Magaña, Martín Cadena-Zapata, Alejandro Zermeño-González, Juan Antonio López-López, and Gilbert Fresh López-López. "Evaluación de un transductor integral para determinar la magnitud del error de medición." REVISTA TERRA LATINOAMERICANA 37, no. 4 (2019): 329. http://dx.doi.org/10.28940/terra.v37i4.466.

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The use of soil reaction force transducers coupled between the tractor and integral tillage implements are in their experimental phase in different parts of the world; however, these developments present measurement errors. The objective of this research was to corroborate the magnitude of error between an Integral Force Sensor (SIF) connected to the tractor three-point hitch and two individual sensors coupled to the work tools to monitor soil reaction forces at the integral implements. SIF was tested under laboratory conditions with calibrated equipment to register force measurements at diffe

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Khan, Javaria Ahmad, and Atif Akbar. "Logistic Kernel: A Sensitive Biomarker for Kidney Cancer by ROC Curve." International Journal of Applied Sciences & Development 2 (October 16, 2023): 120–32. http://dx.doi.org/10.37394/232029.2023.2.13.

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The receiver operating characteristic (ROC) curve is a well-known graphical method to describe the accuracy of a diagnostic test. In this paper, Logistic kernel is proposed with its optimal bandwidth and mean squared error. To observe the performance of our proposed kernel estimator, the comparison is made with a Gaussian kernel by using different bandwidths and ROC curve and the area under the curve (AUC) are calculated. For illustration, Kidney cancer data is used and the logistic kernel is found more pragmatic and sensitive biomarker to detect Kidney cancer. The outstanding performance of l

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Shen, Wei, Zhenhuan Yang, Yiming Ying, and Xiaoming Yuan. "Stability and optimization error of stochastic gradient descent for pairwise learning." Analysis and Applications 18, no. 05 (2019): 887–927. http://dx.doi.org/10.1142/s0219530519400062.

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In this paper, we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under ROC curve (AUC) maximization and minimum error entropy (MEE) principle. Our contribution is twofolded. Firstly, we establish the stability results for SGD for pairwise learning in the convex, strongly convex and non-convex settings, from which genera

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Hu, Jinyou, Shaoxian Gu, Ningyu Wang, et al. "Sensitivity of Three Patient-Specific Quality Assurance Systems to MLC Aperture Errors With Volumetric Modulated Arc Therapy." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211144. http://dx.doi.org/10.1177/15330338221114499.

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Purpose: To compare the sensitivity of ArcCHECK (AC), portal dosimetry (PD), and an in-house logfile-based system (LF) to multileaf collimators (MLC) aperture errors and the ability to identify these errors. Methods and Materials: For 12 retrospective original head and neck volumetric modulated arc therapy (VMAT) plans, MLC aperture errors of ± 0.4mm, ± 1.2mm, ± 2mm, and ± 3mm were introduced for each plan, resulting in 96 plans with errors. AC, PD, and LF were used for the gamma evaluation at 3%/3mm, 3%/2mm, and 2%/2mm criteria. Gradient analysis was used to evaluate the sensitivity to MLC ap

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Gillum, J. G., D. S. Israel, R. B. Scott, M. W. Climo, and R. E. Polk. "Effect of combination therapy with ciprofloxacin and clarithromycin on theophylline pharmacokinetics in healthy volunteers." Antimicrobial Agents and Chemotherapy 40, no. 7 (1996): 1715–16. http://dx.doi.org/10.1128/aac.40.7.1715.

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Five adults completed this four-way randomized crossover study to compare the effects of oral treatment with ciprofloxacin, clarithromycin, and a combination of the two drugs on theophylline pharmacokinetics. The area under the concentration-time curve for theophylline during combination therapy was not different from that for ciprofloxacin alone. Beta error may explain this finding, but any real effect from combination treatment appears to be clinically unimportant.

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Kundu, M., S. Bhattacharyya, M. Karmakar, and P. S. Majumdar. "THREE-POINT AREA METHOD FOR THERMOLUMINESCENCE GLOW CURVE ANALYSIS AND ITS APPLICATION TO THE GLOW PEAK OF K2SRP2O7:PR." Radiation Protection Dosimetry 193, no. 3-4 (2021): 247–58. http://dx.doi.org/10.1093/raddos/ncab048.

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Abstract A method has been proposed to evaluate the kinetic parameters, viz. activation energy ($E$) and order of kinetics ($b$) from a single or isolated thermoluminescence (TL) glow peak. Along with the area under the entire curve, this method uses a set of three arbitrary data points and calculates the partial area under the curve from each point to the endpoint. In this way, the entire information associated with the curve is used and the method is named as ‘Three-Point Area’ (TPA) method. We have applied it successfully on a number of theoretically simulated TL curves generated in One Tra

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Sandmo, Stian Bahr, Andrew S. McIntosh, Thor Einar Andersen, Inga Katharina Koerte, and Roald Bahr. "Evaluation of an In-Ear Sensor for Quantifying Head Impacts in Youth Soccer." American Journal of Sports Medicine 47, no. 4 (2019): 974–81. http://dx.doi.org/10.1177/0363546519826953.

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Background: Wearable sensor systems have the potential to quantify head kinematic responses of head impacts in soccer. However, on-field use of sensors (eg, accelerometers) remains challenging, owing to poor coupling to the head and difficulties discriminating low-severity direct head impacts from inertial loading of the head from human movements, such as jumping and landing. Purpose: To test the validity of an in-ear sensor for quantifying head impacts in youth soccer. Study Design: Descriptive laboratory study. Methods: First, the sensor was mounted to a Hybrid III headform and impacted with

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Tejado, Inés, Blas Vinagre, José Traver, Javier Prieto-Arranz, and Cristina Nuevo-Gallardo. "Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers." Mathematics 7, no. 6 (2019): 530. http://dx.doi.org/10.3390/math7060530.

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The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains,

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Pratiwi, Nining, Ade Riana, and Deni Hardiansyah. "Calculation of the total number of radiation decay of Radiolabelled-Octreotate using non-linear mixed effect models." ITM Web of Conferences 61 (2024): 01023. http://dx.doi.org/10.1051/itmconf/20246101023.

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Non-Linear Mixed Effect (NLME) is a method used in the area under the measured time-activity curve (AUC) calculations. The calculation of an accurate AUC is needed for an accurate determination of the radiation absorbed dose. In NLME, the error model might affect the accuracy of the estimation of the AUC. Therefore, the aim of this study was to determine the effect of error models on AUC calculations using NMLE. The data used in this study were from biokinetic data of the 111In-DOTATATE biodistribution in the tumour collected from the literature. The data were fitted using published bi-exponen

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Li, Fan, Aizhou Yao, LiMin Zhu, Chenlong Yang, and Yang Zhang. "An Accelerated Post-processing Calculation Method of Curved Surface Profile Extraction Based on the Total Focusing Method of Ultrasonic Phased Array." Journal of Physics: Conference Series 2242, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2242/1/012001.

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Abstract Surface profile extraction is a significant process for determining the ultrasonic beam propagation path based on the Fermat’s principle while applying the total focusing method (TFM) of ultrasonic phased array on a curved component immersed in water. However, TFM calculation is time consuming due to the large amounts of data post-processing. Hence, a trade-off between computation cost and precision of result image is must to be made. In this paper, an accelerated total focusing method (ATFM) was proposed to extract the surface profile of an arc-shaped component. The experimental resu

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Moayedi, Hossein, Peren Jerfi Canatalay, Atefeh Ahmadi Dehrashid, Mehmet Akif Cifci, Marjan Salari, and Binh Nguyen Le. "Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility." Land 12, no. 1 (2023): 242. http://dx.doi.org/10.3390/land12010242.

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Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques men

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Tsai, Tzong-Yun, Jeng-Fu You, Yu-Jen Hsu, et al. "A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection." Cancers 13, no. 11 (2021): 2808. http://dx.doi.org/10.3390/cancers13112808.

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(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were strat

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ALANAZI, Fayez, and Aminu SULEIMAN. "Impact of Data Balancing and Feature Engineering on Accident Severity Models." Promet - Traffic&Transportation 37, no. 3 (2025): 665–90. https://doi.org/10.7307/ptt.v37i3.856.

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This study investigates the impacts of feature engineering techniques, including Clustering, Target Encoding and Anomaly Detection, in conjunction with data balancing methods, on the efficacy of machine learning models for predicting road accident severity. Automated Machine Learning (AutoML), Distributed Random Forest (DRF), Boosted Regression Trees (BRT) and Deep Learning models were evaluated on datasets that were balanced using the SMOTE (Synthetic Minority Over-Sampling Technique) and ADASYN (Adaptive Synthetic Sampling) techniques. Evaluation metrics such as Mean Squared Error (MSE), Roo

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Kurniawan, Rudi, and Lukman Sunardi. "Integration of Image Enhancement Technique with DenseNet201 Architecture for Identifying Grapevine Leaf Disease." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 2 (2025): 333–46. https://doi.org/10.30812/matrik.v24i2.4137.

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Early detection of grapevine leaf diseases is crucial for maintaining both the quality and quantity of grape production. Manual identification methods are often ineffective and prone to errors. This research aims to develop a precise and efficient method for classifying grapevine leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the DenseNet201 Deep Convolutional Neural Network (DCNN) architecture. The research methodology involves collecting a dataset of grapevine leaf images affected by black measles, black rot, and leaf blight alongside healthy leaves. Followi

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Hajian-Tilaki, Karim O., and James A. Hanley. "Comparison of Three Methods for Estimating the Standard Error of the Area under the Curve in ROC Analysis of Quantitative Data." Academic Radiology 9, no. 11 (2002): 1278–85. http://dx.doi.org/10.1016/s1076-6332(03)80561-5.

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MUKHERJEE, A. K., N. K. MOHAPATRA, A. V. SURIYA RAO, and P. NAYAK. "Effect of nitrogen fertilization on the expression of slow-blasting resistance in rice." Journal of Agricultural Science 143, no. 5 (2005): 385–93. http://dx.doi.org/10.1017/s0021859605005551.

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The development of rice blast disease in four slow-blasting (SB) genotypes was compared with that in the fast-blasting (FB) genotype Karuna, under natural field epidemics over a period of 3 years at five levels of nitrogen, in order to determine if the application of high doses of nitrogen influenced the expression of disease progress in the SB types. The treatment effects were compared through estimation of nine parameters viz. (i) lesion number (LN); (ii) area under disease progress curve (AUDPC); (iii) relative area under disease progress curve (RAUDPC); (iv) logistic apparent infection rat

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Hamidi, Omid, Lily Tapak, Aarefeh Jafarzadeh Kohneloo, and Majid Sadeghifar. "High-Dimensional Additive Hazards Regression for Oral Squamous Cell Carcinoma Using Microarray Data: A Comparative Study." BioMed Research International 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/393280.

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Microarray technology results in high-dimensional and low-sample size data sets. Therefore, fitting sparse models is substantial because only a small number of influential genes can reliably be identified. A number of variable selection approaches have been proposed for high-dimensional time-to-event data based on Cox proportional hazards where censoring is present. The present study applied three sparse variable selection techniques of Lasso, smoothly clipped absolute deviation and the smooth integration of counting, and absolute deviation for gene expression survival time data using the addi

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Li, Yuyang, Wen Nie, Qihang Li, et al. "Prediction of Landslide Deformation Region Based on the Improved S-Growth Curve Model." Applied Sciences 13, no. 6 (2023): 3555. http://dx.doi.org/10.3390/app13063555.

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Quantitative research on and the prediction of a landslide deformation area is an important point to accurately and comprehensively understand the failure mechanism of landslides and the degree of slope failure. This study uses image processing techniques to quantitatively identify the area and volume of deformation regions during rainfall-type landslide destabilization under multifactor conditions. The findings revealed that (1) an increase in rainfall intensity and slope angle, as well as the existence of slope crest, will accelerate the process of slope instability. In our study, when the r

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Nithyalakshmi, V., Dr R. Sivakumar, and Dr A. Sivaramakrishnan. "Automatic Detection and Classification of Diabetes Using Artificial Intelligence." International Academic Journal of Innovative Research 8, no. 1 (2021): 01–05. http://dx.doi.org/10.9756/iajir/v8i1/iajir0801.

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Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

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Li, Kui, Siyi Liu, Yingli He, and Renyu Ran. "Predictive nomogram of high-risk patients with active tuberculosis in latent tuberculosis infection." Journal of Infection in Developing Countries 18, no. 05 (2024): 732–41. http://dx.doi.org/10.3855/jidc.18456.

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Introduction: The absence of predictive models for early latent tuberculosis infection (LTBI) progression persists. This study aimed to create a screening model to identify high-risk LTBI patients prome to active tuberculosis (ATB) reactivation. Methodology: Patients with confirmed ATB were enrolled alongside LTBI individuals as a reference, with relevant clinical data gathered. LASSO regression cross-validation reduced data dimensionality. A nomogram was developed using multiple logistic regression, internally validated with Bootstrap resampling. Evaluation included C-index, receiver operatin

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