Nevena Rankovic

Assistant professor

Tilburg University Tilburg School of Humanities and Digital Sciences

Netherlands

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Dr. Nevena Rankovic is an Assistant Professor at Tilburg University, Tilburg School of Humanities and Digital Sciences in the Netherlands. Her research focuses on innovative technologies for health diagnostics, machine learning applications in software development, and social determinants of health. Recent contributions include exploring semantic patterns in Agile practices, developing AutoML paradigms for chronic disease detection, and investigating socioeconomic inequalities in health outcomes.

Nevena Rankovic

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Articles (22)

Labeling issues through semantic patterns in open-source Agile practices

Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm

Simple integrated circuit reverse-engineering with deep learning: A proof of concept for automating die-polygon-capturing

Interpretable software estimation with graph neural networks and orthogonal array tunning method

Efficient annotation reduction with active learning for computer vision-based Retail Product Recognition

Explainable data mining model for hyperinsulinemia diagnostics

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Socioeconomic inequality as a predictor of unmet health needs in the older adult population of Serbia

Unveiling the Power of ARIMA, Support Vector and Random Forest Regressors for the Future of the Dutch Employment Market

Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks

Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models

Ensemble Model for Predicting Chronic Non-Communicable Diseases using Latin Square Extraction and Fuzzy-Artificial Neural Networks from 2013 to 2019

On the interpretability of Fuzzy Cognitive Maps

On the interpretability of Fuzzy Cognitive Maps

Unveiling the Power of ARIMA, Support Vector and Random Forest Regressors for the Future of the Dutch Employment Market

COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Convergence rate of Artificial Neural Networks for estimation in software development projects

A Novel UCP Model Based on Artificial Neural Networks and Orthogonal Arrays

Internet Addiction among Secondary School Students Conditioned By Gender and Age

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