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Relevant bibliographies by topics / Auc-Roc

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Academic literature on the topic 'Auc-Roc'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Auc-Roc"

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Hong, Chong Sun, and So Yeon Choi. "ROC curve generalization and AUC." Journal of the Korean Data And Information Science Society 31, no. 4 (2020): 477–88. http://dx.doi.org/10.7465/jkdi.2020.31.4.477.

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Hong, Chong Sun, and Dae Soon Yang. "ROC curve and AUC for linear growth models." Journal of the Korean Data and Information Science Society 26, no. 6 (2015): 1367–75. http://dx.doi.org/10.7465/jkdi.2015.26.6.1367.

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Минин, А. С. "Бинаризация вероятностного прогноза методом ROC AUC". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 104, № 14 (2023): 87–91. http://dx.doi.org/10.18411/trnio-12-2023-789.

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В работе проведено исследование влияния порога бинаризации вероятностного прогноза классификатора k-ближайших соседей на значение метрики ROC AUC. Путем варьирования порога бинаризации прогнозов и расчета ROC AUC выявлен оптимальный порог, при котором достигается максимальное значение метрики. Актуальность работы обусловлена широким практическим применением вероятностных классификаторов и необходимостью преобразования их непрерывных прогнозов в дискретные классы. Целью исследования является нахождение оптимального значения порога бинаризации для конкретного классификатора и набора данных на ос

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Krupinski, Elizabeth A. "Evaluating AI Clinically—It’s Not Just ROC AUC!" Radiology 298, no. 1 (2021): 47–48. http://dx.doi.org/10.1148/radiol.2020203782.

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Mukhametshin, Rustam F., Olga P. Kovtun, and Nadezhda S. Davydova. "Respiratory parameters as a predictor of hospital outcomes in newborns requiring medical evacuation." Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care 12, no. 4 (2023): 441–52. http://dx.doi.org/10.17816/psaic1292.

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BACKGROUND: Assessment of the clinical condition, prediction of risks and possible outcomes during the transfer of newborns remains an important part of the work of transport teams. Respiratory disorders remain a significant indication for transfer to medical organizations of a higher level of care. AIM: To study the predictive value of the parameters of respiratory support in newborns requiring medical evacuation for the outcomes of treatment. MATERIALS AND METHODS: The observational, cohort, retrospective study included data from neonatal to patients on ventilators (286 newborns)

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Mahmud, Fuad, Badruddowza Badruddowza, Md Shohail Uddin Sarker, et al. "ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION." American Journal of Engineering and Technology 06, no. 10 (2024): 86–99. http://dx.doi.org/10.37547/tajet/volume06issue10-10.

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This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model fo

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Kim, Seong-Jin, Xue-Cheng Jin, Rajaraman Bharanidharan, and Na-Yeon Kim. "Monitoring Multiple Behaviors in Beef Calves Raised in Cow–Calf Contact Systems Using a Machine Learning Approach." Animals 14, no. 22 (2024): 3278. http://dx.doi.org/10.3390/ani14223278.

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The monitoring of pre-weaned calf behavior is crucial for ensuring health, welfare, and optimal growth. This study aimed to develop and validate a machine learning-based technique for the simultaneous monitoring of multiple behaviors in pre-weaned beef calves within a cow–calf contact (CCC) system using collar-mounted sensors integrating accelerometers and gyroscopes. Three complementary models were developed to classify feeding-related behaviors (natural suckling, feeding, rumination, and others), postural states (lying and standing), and coughing events. Sensor data, including tri-axial acce

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Muschelli, John. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric." Journal of Classification 37, no. 3 (2019): 696–708. http://dx.doi.org/10.1007/s00357-019-09345-1.

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Abbas, Adeel, Farkhanda Abbas, Fazila Abbas, Abdulwahed Fahad Alrefaei, and Mohammed Fahad Albeshr. "Enhancing Landslide Prediction: A Comparative Study of Ensembled and Non-Ensembled Machine Learning Approaches with Dimensionality Reduction and Random Feature Selection to Showcase Entropy Management." Journal of Sensor Networks and Data Communications 4, no. 3 (2024): 01–23. https://doi.org/10.33140/jsndc.04.03.04.

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This research looks at how well different ensembled and non-ensembled machine learning algorithms perform both before and after dimensionality reduction and manual feature engineering using random feature selection. LightGBM, Extra Trees (EXT), XGBoost, Gradient Boosting Machine (GBM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree (DT) are among the algorithms that were assessed. With a computational time (CT) of 15.985 seconds prior to dimensionality reduction, LightGBM obtained an AUC/ROC score of 0.833, whereas Extra Trees (EXT), XGBoost, and GBM each ob

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Vishnu, Vardhan R*, and S. Balaswamy. "COMPARING SEVERAL DIAGNOSTIC PROCEDURES USING THE INTRINSIC MEASURES OF ROC CURVE." Indian Journal of Medical Research and Pharmaceutical Sciences 3, no. 3 (2016): 48–55. https://doi.org/10.5281/zenodo.47521.

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Comparison of diagnostic tests is essential in medicine. Test procedures for comparing two or more ROC curves are all based on measures d<sup>'</sup>, AUC and the maximum likelihood estimates of binormal ROC curves. However, intrinsic measures such as sensitivity and specificity also play a pivotal role in assessing the performance of several diagnostic procedures. In this paper, a new methodology is proposed in order to compare several diagnostic procedures using the intrinsic measures of ROC curve

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Dissertations / Theses on the topic "Auc-Roc"

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Zheng, Shimin. "The ROC Curve and the Area under the Curve (AUC)." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/139.

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Lu, Qing. "Methods for Designing and Forming Predictive Genetic Tests." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212197560.

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Yuan, Yan. "Empirical Likelihood-Based NonParametric Inference for the Difference between Two Partial AUCS." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/32.

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Compare the accuracy of two continuous-scale tests is increasing important when a new test is developed. The traditional approach that compares the entire areas under two Receiver Operating Characteristic (ROC) curves is not sensitive when two ROC curves cross each other. A better approach to compare the accuracy of two diagnostic tests is to compare the areas under two ROC curves (AUCs) in the interested specificity interval. In this thesis, we have proposed bootstrap and empirical likelihood (EL) approach for inference of the difference between two partial AUCs. The empirical likelihood rat

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Huang, Xin. "Bootstrap and Empirical Likelihood-based Semi-parametric Inference for the Difference between Two Partial AUCs." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/54.

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With new tests being developed and marketed, the comparison of the diagnostic accuracy of two continuous-scale diagnostic tests are of great importance. Comparing the partial areas under the receiver operating characteristic curves (pAUC) is an effective method to evaluate the accuracy of two diagnostic tests. In this thesis, we study the semi-parametric inference for the difference between two pAUCs. A normal approximation for the distribution of the difference between two pAUCs has been derived. The empirical likelihood ratio for the difference between two pAUCs is defined and its asymptoti

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Sun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.

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Thesis (M.S.)--Georgia State University, 2008.<br>Title from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).

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Zhou, Haochuan. "Statistical Inferences for the Youden Index." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/5.

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In diagnostic test studies, one crucial task is to evaluate the diagnostic accuracy of a test. Currently, most studies focus on the Receiver Operating Characteristics Curve and the Area Under the Curve. On the other hand, the Youden index, widely applied in practice, is another comprehensive measurement for the performance of a diagnostic test. For a continuous-scale test classifying diseased and non-diseased groups, finding the Youden index of the test is equivalent to maximize the sum of sensitivity and specificity for all the possible values of the cut-point. This dissertation concentrates

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Xu, Ping. "Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4261.

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Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asym

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Bitara, Matúš. "Srovnání heuristických a konvenčních statistických metod v data miningu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400833.

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The thesis deals with the comparison of conventional and heuristic methods in data mining used for binary classification. In the theoretical part, four different models are described. Model classification is demonstrated on simple examples. In the practical part, models are compared on real data. This part also consists of data cleaning, outliers removal, two different transformations and dimension reduction. In the last part methods used to quality testing of models are described.

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Khamesipour, Alireza. "IMPROVED GENE PAIR BIOMARKERS FOR MICROARRAY DATA CLASSIFICATION." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1573.

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The Top Scoring Pair (TSP) classifier, based on the notion of relative ranking reversals in the expressions of two marker genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. We introduce the AUC-based TSP classifier, which is based on the Area Under the ROC (Receiver Operating Characteristic) Curve. The AUCTSP classifier works according to the same principle as TSP but differs from the latter in that the probabilities that determine the top scoring pair are computed based on the relati

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Wang, Binhuan. "Statistical Evaluation of Continuous-Scale Diagnostic Tests with Missing Data." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/8.

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The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of diagnostics tests or bio-markers. Currently most widely used statistical methods for the inferences of ROC curves are complete-data based parametric, semi-parametric or nonparametric methods. However, these methods cannot be used in diagnostic applications with missing data. In practical situations, missing diagnostic data occur more commonly due to various reasons such as medical tests being too expensive, too time consuming or too invasive. This dissertation aims to

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Book chapters on the topic "Auc-Roc"

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Klawonn, Frank, Frank Höppner, and Sigrun May. "An Alternative to ROC and AUC Analysis of Classifiers." In Advances in Intelligent Data Analysis X. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24800-9_21.

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Gentili, Elisabetta, Alice Bizzarri, Damiano Azzolini, Riccardo Zese, and Fabrizio Riguzzi. "Regularization in Probabilistic Inductive Logic Programming." In Inductive Logic Programming. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49299-0_2.

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AbstractProbabilistic Logic Programming combines uncertainty and logic-based languages. Liftable Probabilistic Logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, called LIFTCOVER+, in which regularization was added to improve the quality of the solutions and LBFGS was replaced by gradient descent. We tested

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Marcus, Pamela M. "Performance Measures." In Assessment of Cancer Screening. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94577-0_3.

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AbstractPerformance measures reflect the link between cancer screening test results and cancer diagnoses. They measure the ability of cancer screening to lead to detection of cancer, and provide no evidence as to screening’s ability to reduce mortality. Performance measures are rarely considered sufficient evidence to implement cancer screening for the first time, though they have driven dissemination of tests that are thought to represent upgrades of established cancer screening tests. Chapter 3 presents the six key performance measures: sensitivity, specificity, positive predictive value, ne

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Van Duong, Binh, Igor K. Fomenko, Denis N. Gorobtsov, et al. "An Integration of the Fractal Method and the Statistical Index Method for Mapping Landslide Susceptibility." In Progress in Landslide Research and Technology, Volume 3 Issue 1, 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55120-8_30.

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AbstractAppropriate land use planning and the sustainable development of residential communities play a crucial role in the development of mountainous provinces in Vietnam. Because these regions are especially prone to natural disasters, including landslides, landslide studies can provide valuable data for determining the evolution of the landslide process and assessing landslide risk. This study was conducted to assess landslide susceptibility in Muong Khoa commune, Son La province, Vietnam, using the Statistical Index method (SI) and the integration of the Fractal method and Statistical Inde

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Ishihara, Hideyuki, Fumiaki Oka, Takuma Nishimoto, Masatoshi Yamane, Kazutaka Sugimoto, and Hirokazu Sadahiro. "ADC Threshold Indicating the Ischemic Region for Predicting Efficacy in Thrombectomy." In Acta Neurochirurgica Supplement. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89844-0_16.

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Abstract Purpose: The effectiveness of endovascular thrombectomy (EVT) has been proven in patients with large cerebral infarction. However, the size of the ischemic region before treatment is a significant factor in the outcome, and the optimal method for the evaluation of this region is uncertain. The goal of this study was to investigate apparent diffusion coefficient (ADC) values as a basis for an assessment of the ischemic region before treatment. Methods: A retrospective study was performed in 48 consecutive patients who underwent EVT for acute large vessel occlusion (LVO) with Alberta St

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Koncar, Philipp, and Denis Helic. "Employee Satisfaction in Online Reviews." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60975-7_12.

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Abstract Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction.

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De Bruijn Berry. "Revisiting the Area Under the ROC." In Studies in Health Technology and Informatics. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-806-9-532.

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The Receiver-Operating Characteristic curve or ROC has been a long standing and well appreciated tool to assess performance of classifiers or diagnostic tests. Likewise, the Area Under the ROC (AUC) has been a metric to summarize the power of a test or ability of a classifier in one measurement. This article aims to revisit the AUC, and ties it to key characteristics of the noncentral hypergeometric distribution. It is demonstrated that this statistical distribution can be used in modeling the behaviour of classifiers, which is of value for comparing classifiers.

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Kant, Vishnu, Kanwarpartap Singh Gill, Mukesh Kumar, and Ruchira Rawat. "Safeguarding Finances: State-of-the-Art Fraud Detection Methods for Credit Cards." In Applied Intelligence and Computing. Soft Computing Research Society, 2024. http://dx.doi.org/10.56155/978-81-955020-9-7-12.

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This research primarily aims to shed light on the serious issue of credit card fraud, which has become much worse with the advent of internet shopping and, more specifically, the present COVID-19 epidemic. Developing a machine learning system capable of distinguishing between legitimate and fraudulent credit card transactions is the primary objective of this project, which aims to decrease an annual loss of $24 billion. Using data such as transformed numerical characteristics after PCA analyses, transaction time and amount, and Logistic Regression, Decision Tree Classifier, and K-Nearest Neigh

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Katostaras Theofanis and Katostara Niki. "Area of the ROC curve when one point is available." In Studies in Health Technology and Informatics. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-276-9-219.

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In this paper, the method of extension is proposed for the calculation of the area under the ROC curve, if only one pair of specificity (Sp0=1-FP0) and sensitivity (Se0) is available. The method of extension is algebraic and is based on the assumption that the ROC curve is continuous, concave, truly ascending and has the form Se=&kappa;0+&kappa;1FP+&kappa;2FP2in parts. The area under ROC curve is calculated by the formula: AUC=&lpar;7Sp0+7Se0&minus;2Sp0Se0&rpar;/12.

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K., Muthamil Sudar, P. Nagaraj, and P. Vaissnave. "Application of Machine Unlearning Techniques to Enhance the Performance and Adaptability of DDoS Attack Detection Models." In Advanced Cyber Defense for Space Missions and Operations. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7939-4.ch013.

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Traditional detection models may be accurate at first, but they quickly lose effectiveness as patterns of attacks change. They therefore require frequent and resource-intensive retraining. To achieve this, the authors have adopted several machine unlearning techniques, such as incremental unlearning, selective forgetting, recurrent unlearning, and adversarial unlearning, which allow a model to forget outdated information dynamically and learn from the new relevant traffic patterns. They assessed the performance of the models in terms of accuracy, precision, recall, F1-score, AUC-ROC, Matthews

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Conference papers on the topic "Auc-Roc"

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Bahaweres, Rizal Broer, and Islah Khofifah Nuraini. "Cost-sensitive Approach for improving AUC-ROC Curve of Software Defect Prediction." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10668184.

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Muhammad-Haseeb-Zia, Ali Hussain, and Muhammad-Hamza. "Comparative Analysis of Random Forest and Support Vector Machine Classifiers for unjustified malware detection of Android Devices Data Consuming SMOTE and ROC-AUC Metrices." In 2024 Horizons of Information Technology and Engineering (HITE). IEEE, 2024. https://doi.org/10.1109/hite63532.2024.10777139.

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López Flores, Walter Jeremías. "Evaluation of Neural Network and Logit Models for Classification of Default in Banking Loans." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-dxrv7c.

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The purpose of the study was to evaluate the performance of neural networks as modern techniques to classify the risk of default against the traditional Logit statistical method, taking a Honduran bank as a case study. The data was obtained from its credit portfolio made up of 38,156 personal loans and 9 available characteristics, choosing the most representative independent variables to design a Multilayer Perceptron type base model and its Logit equivalent to which characteristics were added to analyze their impact on the classification of the dependent variable Default, leaving in the end a

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Shekter, Dylan H., and Frank W. Samuelson. "Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples." In Image Perception, Observer Performance, and Technology Assessment, edited by Frank W. Samuelson and Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2550601.

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Hong, Shenda, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, and Jimeng Sun. "RDPD: Rich Data Helps Poor Data via Imitation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/817.

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In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-feature representations. On the other hand, an environment with poor observation equipment (e.g., at home) only provides low-quality, uni-modal data with poor-feature representations. To deploy a competitive model in a poor-data environment without requiring direct access to mult

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Ferris, Michael H., Michael McLaughlin, Samuel Grieggs, et al. "Using ROC curves and AUC to evaluate performance of no-reference image fusion metrics." In NAECON 2015 - IEEE National Aerospace and Electronics Conference. IEEE, 2015. http://dx.doi.org/10.1109/naecon.2015.7443034.

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Santos, Micael S., Gean S. Santos, and Andre L. L. Aquino. "Identificação do Comportamento de Motoristas: Uma Abordagem Baseada em Teoria da Informação." In Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbcup.2024.2389.

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Neste trabalho, propomos a identificação do comportamento do motorista com o uso do algoritmo Random Forest e Long Short-Term Memory (LSTM), baseado em medidas de teoria da informação, como Entropia de Shannon, Complexidade Estatística e Informação de Fisher. Os modelos LSTM e Random Forest foram aplicados em dados provenientes dos sensores acelerômetro e giroscópio em veículos. Tais dados foram rotulados como: slow, normal, e aggressive. Comparamos a metodologia padrão da literatura com a nossa proposta por meio das medidas de acurácia, área sob a curva ROC (AUC), e precisão. Seguindo a liter

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Chaves, Rubens Marques, André Luis Debiaso Rossi, and Luís Paulo Faina Garcia. "A Financial Distress Prediction using a Non-stationary Dataset." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234013.

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Financial distress prediction (FDP) is crucial to companies, investors, and authorities. However, most FDP studies have been based on stationary models, disregarding important challenges present on financial distress data such as non-stationarity. Therefore, the lack of real-world datasets of economic-financial indicators organized in a timeline manner is a gap to be addressed. This study proposes a comprehensive dataset of 84 economic-financial indicators from the Brazilian Securities and Exchange Commission (CVM) organized in a non-stationary manner and validated by experiments using classif

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MIKHALEV, A. S., V. A. ZHUKOVSKAYA, M. A. KOBELEV, A. V. PYATAEVA, and A. E. GETZ. "STUDY OF THE EFFECTIVENESS OF MODERN CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES IN THE TASK OF RECOGNIZING SKIN NEOPLASMS." In GRAPHICON 2024. Omsk State Technicl University, 2024. http://dx.doi.org/10.25206/978-5-8149-3873-2-2024-629-635.

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The article deals with the problem of increasing incidence of skin cancer, especially melanoma, which is the most aggressive and dangerous form of this disease. The main attention is paid to innovative technologies for diagnosing skin neoplasms, in particular, the use of artificial intelligence. The study analyzes the effectiveness of different convolutional neural network architectures for recognizing skin neoplasms based on digital images. The raw data includes 25331 images of different types of skin lesions from the ISIC 2019 dataset. For training and testing, data augmentation techniques w

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Gantert, Luana, and Miguel Elias M. Campista. "Seleção de Modelo de Aprendizado Federado Baseado em Busca e Poda para Detecção de Defeitos Industriais." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sbrc.2024.1548.

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O aprendizado federado surge como uma alternativa ao aprendizado de máquina tradicional ao descentralizar o treinamento dos modelos. Os dispositivos clientes se comunicam com um servidor central e treinam o modelo definido de maneira iterativa. A definição antecipada do modelo a ser treinado, porém, é um desafio pouco discutido. Este trabalho propõe a seleção da melhor rede neural a partir de um procedimento de busca envolvendo múltiplas redes, e posterior poda daquelas que se mostrarem menos promissoras durante o treinamento. Para isso, em uma dada rodada de avaliação, os nós enviam os result

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Reports on the topic "Auc-Roc"

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โตสุโขวงศ์, ปิยะรัตน์, จุลินทร์ โอภานุรักษ์, สุพจน์ รัชชานนท์, ชาญชัย บุญหล้า та อภิวัฒน์ มุทิรางกูร. โครงการวิจัยนำร่องมะเร็งกระเพาะปัสสาวะในผู้สูงอายุ : ผลของการปรับเปลี่ยนการดำเนินชีวิตร่วมกับการให้มะนาวผงกับขมิ้นชัน ต่อการเปลี่ยนแปลงของการแสดงออกของยีน ภาวะเหนือพันธุกรรมของจีโนม ตัวบ่งชี้ทางชีวภาพ และอัตราการกลับเป็นซ้ำ : รายงานผลการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2011. https://doi.org/10.58837/chula.res.2011.27.

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วัตถุประสงค์: ภาวะเครียดจากออกซิเดชั่นและการเปลี่ยนแปลงแบบเหนือพันธุกรรมเกี่ยวข้องกับการเกิดมะเร็งกระเพาะปัสสาวะ โดยผู้วิจัยตั้งสมมติฐานว่าระดับภาวะเครียดจากออกซิเดชั่นน่าจะสัมพันธ์กับการลดลงของระดับเมทิลเลชั่น ดังนั้นการศึกษานี้จึงมีวัตถุประสงค์เพื่อประเมินความสัมพันธ์กระหว่างระดับการเกิด LINE1 methylation กับระดับตัวบ่งชี้ภาวะเครียดจากออกซิเดชั่นในผู้ป่วยมะเร็งกระเพาะปัสสาวะของเซลล์ในปัสสาวะ และศึกษาคุณค่าทางคลินิกในการวินิจฉัยโรคมะเร็งกระเพาะปัสสาวะ วิธีการศึกษา: ผู้ป่วยมะเร็งกระเพาะปัสสาวะจำนวน 61 ราย และคนปกติจำนวน 45 ราย แล้ววัดระดับการเกิด methylation ทั้งหมด รวมทั้ง mCuC(partially meth

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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f

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Chen, Xiaole, Peng Wang, Yunquan Luo, et al. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.

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Objective: The aim of this study was to assess the therapeutic effects of Jianpi Liqi decoction (JPLQD) in hepatocellular carcinoma (HCC) and explore its underlying mechanisms. Methods: The characteristics and outcomes of HCC patients with intermediate stage B who underwent sequential conventional transcatheter arterial chemoembolization (cTACE) and radiofrequency ablation (RFA) only or in conjunction with JPLQD were analysed retrospectively. The plasma proteins were screened using label-free quantitative proteomics analysis. The effective mechanisms of JPLQD were predicted through network pha

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