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981
Skin Lesion Image Segmentation Algorithm Based on MC-UNet
Published 2025-01-01“…Aiming at the situation of dermatoscopic images with fuzzy lesion boundaries, variable morphology and high similarity to background, this paper proposes a skin lesion segmentation algorithm that achieves higher segmentation accuracy by combining existing convolutional neural network methods. …”
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982
Path integral for multiplicative noise: Generalized Fokker-Planck equation and entropy production rate in stochastic processes with threshold
Published 2025-05-01“…Then, using the Parisi-Sourlas method, we estimate the transition probability between states of a stochastic variable [X(t)] based on the cumulant-generating function (K_{η}) of the noise. …”
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983
Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method
Published 2015-01-01“…The optimal control indicator is determined via current state of converter smelting production process, and the set values of operation variable would control converter production. Relationship between various operating variables, current temperature, and carbon content is constructed through operation analysis of a great deal of actual production data; then, the dynamic optimal control indicator is derived from historical excellent smelting data; finally, the dynamic operation optimization model is built by taking the minimum deviation between the current data—molten steel temperature and carbon content—and optimal data which are determined by the optimal control indicator as objective function. …”
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984
Parametric Survival Models of Hemodialysis Patients in Relation with Patient-Related Factors
Published 2020-12-01“…Based on the Cox-Snell Residuals and AIC, BIC, and Gompertz (PH) model is an efficient model than other when the values of (AIC=662.21), (BIC=703.83) and (R2=0.211) where maintained Study assessed that the variables dealing with univariate models were signifi cant but had a signifi cant effect on hemodialysis survival. …”
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985
Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites
Published 2024-10-01“…We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. …”
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986
Cognitive performance classification of older patients using machine learning and electronic medical records
Published 2025-02-01“…Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. …”
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987
Fiscal Sustainability in Iran A Markov-Switching Fiscal Regime Change and Time-Varying Parameter Models Approach
Published 2025-03-01“…Then the Markov-switching fiscal regime change model and the time-varying parameter fiscal reaction function are estimated. Among the constant parameter linear and non-linear models, only the linear model including control variables has a significant debt-to-GDP ratio coefficient, which its sign is negative. …”
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988
UNFOLDING FACTORS INFLUENCING PINEAPPLE PRODUCTION: EMPIRICAL EVIDENCE FROM BAGAMOYO IN TANZANIA
Published 2025-03-01“…The coefficient of determination 𝑅 ଶ = 0.81 showed that the explanatory variables were fit for the model as 81% of the variation in the production of pineapples is explained by the selected seven factors. …”
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989
On practical aspects of optimal modelling in boundary element method
Published 2001-01-01“…Since the optimal discretization theory derived for the function of one variable, then only the 2D problem is considered. …”
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990
Attributes of Internal Audit and Prevention, Detection and Assessment of Fraud in Pakistan
Published 2020-10-01“… With it being considered as a value-added activity, the Internal Audit function (IAF) of a firm is one of the most important functions in an organization. …”
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991
Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy
Published 2025-06-01“…Confusion matrix analysis further confirmed RF as the optimal model, achieving 0.875 accuracy and robust inter-rater agreement (Cohen's kappa coefficient = 0.696) in the testing cohort. SHAP analysis identified the adenoid-to-nasopharyngeal ratio as the predominant diagnostic indicator, followed by tympanometric type and history of recurrent respiratory infections.ConclusionAn RF-based diagnostic model effectively identifies OME in AH children by integrating anatomical, functional, and inflammatory parameters, providing a clinically applicable tool for enhanced diagnostic accuracy and evidence-based management decisions.…”
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992
Fuzzy Goal Programming Procedure to Bilevel Multiobjective Linear Fractional Programming Problems
Published 2010-01-01“…In the proposed procedure, the membership functions for the defined fuzzy goals of the decision makers (DMs) objective functions at both levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by first-level decision maker are developed first in the model formulation of the problem. …”
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993
Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models
Published 2025-07-01“…The high precision and Phi Correlation Coefficient further affirm the reliability and accuracy of the model’s predictions. …”
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994
Testing the Level of Alternative Institutions as a Slowdown Factor of Economic Development: the Case of Montenegro
Published 2017-05-01“…On the basis of the conducted statistical examines: standard error of the regression estimate, correlation coefficient, and coefficient of determination are calculated on the basis of previously determined regression coefficients and forecast values of the linear function of free variables (factors: a, b, and c). …”
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995
Transfer Robustness Optimization for Urban Rail Transit Timetables
Published 2018-01-01“…Next, to improve the computational efficiency, we propose an approximate linearization approach for the EETC function and introduce two types of binary variables and auxiliary substitution variables to convert the nonlinear model to a mixed-integer linear model. …”
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996
Selected Aspects of Heat Transfer Study in a Gun Barrel of an Anti-Aircraft Cannon
Published 2023-06-01“…In each zone, the heat transfer coefficient, as a function of the time hi(t) and bore gas temperature as a function of the time Tg(t) to the cannon barrel for given ammunition parameters, was developed. …”
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997
Empirical Study of the Impact of Unemployment on Political Stability in Iran (1985-2015)
Published 2021-06-01“…Vector regression (VAR), Johansson-Juselius, Vector Error Correction Model (VECM), and variance analysis function were used for this purpose. The results show that in the long run, two variables of unemployment rate and Gini coefficient have a negative and significant relationship with political stability. …”
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998
Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches
Published 2025-08-01“…Abstract This study used data from a large dam site to model changes in groundwater quality variables. Several indicators were investigated to check the quality of water sources for the site for different purposes. …”
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999
A New Multi-Parametric MRI-Based Scoring System for Degenerative Cervical Myelopathy: The Severity on Imaging Myelopathy Score (SIMS)
Published 2025-05-01“…<b>Background/Objectives:</b> Degenerative cervical myelopathy (DCM) is the leading cause of functional disabilities of spinal origin in people over 50 years old. …”
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1000
Pareto Optimal Solutions for Stochastic Dynamic Programming Problems via Monte Carlo Simulation
Published 2013-01-01“…Instead of using the expected values of the objective function, the randomness nature of the decision variables is kept along the process, while Pareto fronts weighted by all quantiles of the objective function are determined. …”
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