Tome Eftimov
Journal articlesshow recent
User-Defined Trade-Offs in LLM Benchmarking: Balancing Accuracy, Scale, and Sustainability, KBS (2025)Exploring Module Interactions in Modular CMA-ES across Problem Classes , SWARM EVOL COMPUT (2025)
Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data, PROTEOMICS (2025)
Comprehensive Benchmarking of Knowledge Graph Embeddings Methods for Android Malware Detection, EXPERT SYST APPL (2025)
A Learning Search Algorithm for the Restricted Longest Common Subsequence Problem, EXPERT SYST APPL (2025)
Landscape Features in Single-Objective Continuous Optimization: Have We Hit a Wall in Algorithm Selection Generalization?, SWARM EVOL COMPUT (2025)
Benchmarking Footprints of Continuous Black-Box Optimization Algorithms: Explainable Insights into Algorithm Success and Failure, SWARM EVOL COMPUT (2025)
Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization Evolutionary Computation, ECJ (2025)
IsoFoodTrack: A Comprehensive Database and Management System Based on Stable Isotope Ratio Analysis for Combating Food Fraud, Frontiers in Nutrition (2025)
Zero-shot evaluation of ChatGPT for food named-entity recognition and linking, Frontiers in Nutrition (2024)
Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks, ECJ (2024)
Opt2Vec - A continuous optimization problem representation based on the algorithm: a case study on problem classification, INFORM SCIENCES (2024)
A Cross-Benchmark Examination of Feature-based Algorithm Selector Generalization in Single-Objective Numerical Optimization, SWARM EVOL COMPUT (2024)
NutriGreen image dataset: a collection of annotated nutrition, organic, and vegan food products, Frontiers in Nutrition (2024)
TinyTLA: Topological landscape analysis for optimization problem classification in a limited sample setting, SWARM EVOL COMPUT (2024)
MsGEN: Measuring Generalization of Nutrient Value Prediction across Different Recipe Datasets, ESWA (2024)
FooDis: A food-disease relation mining pipeline, Artificial intelligence in Medicine (2023)
From Language Models to Large-scale Food and Biomedical Knowledge Graphs, SCI REP-UK (2023)
OPTION: OPTImization Algorithm Benchmarking ONtology, IEEE TECV (2023)
Towards understanding the importance of time-series features in automated algorithm performance prediction, ESWA (2023)
CafeteriaSA corpus: Scientific abstracts annotated across different food semantic resources, DATABASE (2022)
CafeteriaFCD corpus: Food consumption data annotated with regard to different food semantic resources, FOODS (2022)
Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction, ESWA (2022)
Missing value imputation in Food Composition Data with Denoising Autoencoders, J FOOD COMPOS ANAL (2022)
In-depth Insights into Alzheimer’s Disease by using Explainable Machine Learning Approach, SCI REP-UK (2022)
Responsible Governance for a Food and Nutrition E-Infrastructure: Case Study of the Determinants and Intake Data Platform, FRONT NUTR (2022)
Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection, MATHEMATICS (2022)
Less is more: Selecting the right benchmarking set of data for time series classification, EXPERT SYST APPL (2022)
Data-driven Intelligence System for General Recommendations of Deep Learning Architectures, IEEE ACCESS (2021)
Domain Heuristic Fusion of Multi-word Embeddings for Nutrient Value Prediction, MATHEMATICS (2021)
Designing a research infrastructure (RI) on food behaviour and health: Balancing user needs, business model, governance mechanisms and technology, TRENDS FOOD SCI TECH (2021)
A Fine-Tuned Bidirectional Encoder Representations From Transformers for Food Named-Entity Recognition: Algorithm Development and Validation, JMIR (2021)
Impact of COVID-19 confinement on eating behaviours across 16 European countries: the COVIDiet cross-national study, FOOD QUAL PREFER (2021)
A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures, BIG DATA RES (2021)
Deep Statistical Comparison for Multi-objective Stochastic Optimization Algorithms, SWARM EVOL COMPUT (2021)
Optimising an FFQ using a machine learning pipeline to learn an efficient nutrient intake predictive model, NUTRIENTS (2020)
P-NUT: Predicting NUTrient content from short text descriptions, MATHEMATICS (2020)
DietHub: Dietary Habits Analysis through Understanding the Content of Recipes, TRENDS FOOD SCI TECH (2020)
MAKEDONKA: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language, APPL SCI-BASEL (2020)
COVID-19 pandemic changes the food consumption patterns, TRENDS FOOD SCI TECH (2020)
Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool, MATHEMATICS (2020)
Fatty Acid and Stable Carbon Isotope Composition of Slovenian Milk: Year, Season, and Regional Variability, MOLECULES (2020)
Geographical verification of Slovenian milk using stable isotope ratio, multi-element and multivariate modelling approaches, FOOD CHEM (2020)
Multi-objective optimization benchmarking using DSCTool, MATHEMATICS (2020)
Evaluating Missing Value Imputation Methods for Food Composition Databases, FOOD CHEM TOXICOL (2020)
A survey of named-entity recognition methods for food information extraction, IEEE ACCESS (2020)
FoodEx2vec: New foods’ representation for advanced food data analysis, FOOD CHEM TOXICOL (2020)
Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis, APPL SOFT COMPUT (2020)
Multi-level information fusion for learning a blood pressure predictive model using sensor data, INFORM FUSION (2020)
DSCTool: a web services based framework for statistical comparison of stochastic optimization algorithms, APPL SOFT COMPUT (2020)
Selected elements and fatty acid composition in human milk as indicators of seafood dietary habits, ENVIRON RES (2020)
Identifying Practical Significance Through Statistical Comparison of Meta-heuristic Stochastic Optimization Algorithms, APPL SOFT COMPUT (2019)
FoodBase corpus: a new resource of annotated food entities, DATABASE (2019)
MIGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases, APPL SCI-BASEL (2019)
A Novel Statistical Approach for Comparing Meta-heuristic Stochastic Optimization Algorithms According to the Distribution of Solutions in the Search Space, INFORM SCIENCES (2019)
ISO-FOOD Ontology: A formal representation of the knowledge within the domain of Isotopes for Food Science, FOCH (2019)
Mixed Deep Learning and Natural Language Processing Method for Fake Food Image Recognition and Standardization to Help Automated Dietary Assessment, PUBLIC HEALTH NUTR (2018)
Identification of requirements for computer-supported matching of food consumption data with food composition data, Nutrients (2018)
A Novel Approach to Statistical Comparison of Meta-heuristic Stochastic Optimization Algorithms using Deep Statistics, Information Sciences (2017)
A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations, PLoS One (2017)
StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2, Nutrients (2017)
Random Access Protocols With Collision Resolution in a Noncoherent Setting, IEEE Wireless Communications Letters (2015)
Finite-SNR bounds on the sum-rate capacity of Rayleigh block-fading multiple-access channels with no a priori CSI, IEEE Trans. Commun. (2015)
Conference papersshow all
Quantifying Module Interactions in the PSO-X Framework, AutoML 2025FoodSEM: Large Language Model Specialized in Food Named-Entity Linking, DS 2025
Aligning Food Ingredients with Multiple Semantic Resources, ICT Innovations 2024
Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction, GECCO 2025
Comparing Optimization Algorithms Through the Lens of Search Behavior Analysis, GECCO 2025
Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance, GECCO 2025
Adaptive Estimation of the Number of Algorithm Runs in Stochastic Optimization, GECCO 2025
A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization, IEEE CEC 2025
ClustOpt: A Clustering-based Approach for Representing and Visualizing the Search Dynamics of Numerical Optimization Algorithms, IEEE CEC 2025
Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes, IEEE CEC 2025
Efficient Search Algorithms for the Restricted Longest Common Subsequence Problem, ICCS 2024
TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization, GECCO 2024
Per-Run Algorithm Performance Improvement Forecasting Using Exploratory Landscape Analysis Features: A Case Study in Single-Objective Black-Box Optimization, GECCO 2024
Comparing Solvability Patterns of Algorithms across Diverse Problem Landscapes, GECCO 2024
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization, GECCO 2024
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks, IEEE CEC 2024
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks, IEEE CEC 2024
Impact of Scaling in ELA Feature Calculation on Algorithm Selection Cross-Benchmark Transferability, IEEE CEC 2024
Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization, IEEE SSCI 2023
Enhancing Food Composition Databases: Predicting Missing Values via Knowledge Graph Embeddings, SIKDD
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization, AutoML 2023
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction, IEEE CEC 2023
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems, GECCO 2023
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems, GECCO 2023
Assessing the Generalizability of a Performance Predictive Model, GECCO 2023
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances, GECCO 2023
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms, EvoApps 2023
RF+clust for Leave-One-Problem-Out Performance Prediction, EvoApps 2023
Books & Chaptersshow all
Explainable Landscape Analysis, Explainable AI for Evolutionary Computation, Nature Computing Series, Springer Singapore (2025)Neurosymbolic Methods for Food Computing, Volume 400: Handbook on Neurosymbolic AI and Knowledge Graphs, Frontiers in Artificial Intelligence and Applications (2025)
Other publicationsshow all
Importance of Complete Food Composition Databases and Computer Methods for dealing with Missing Food Composition Data (2019)Quality Improvement of Food Composition Databases Using Methods from Natural Language Processing and Statistics (2019)