Tome Eftimov is a Postdoctoral Research Fellow at the Center for Population Health Sciences. He received his Ph.D. in Information and Communication Technologies from the Jožef Stefan International Postgraduate School, Ljubljana, Slovenia.
Bachelor of Elec Engineering, Univerzitet Sv Kiril I Metodi (2012)
Master of Engineering, Univerzitet Sv Kiril I Metodi (2014)
Doctor of Philosophy, Jožef Stefan International Postgraduate School, Information and Communication Technologies (2018)
Mark Cullen, Postdoctoral Faculty Sponsor
ISO-FOOD ontology: A formal representation of the knowledge within the domain of isotopes for food science
2019; 277: 382–90
To link and harmonize different knowledge repositories with respect to isotopic data, we propose an ISO-FOOD ontology as a domain ontology for describing isotopic data within Food Science. The ISO-FOOD ontology consists of metadata and provenance data that needs to be stored together with data elements in order to describe isotopic measurements with all necessary information required for future analysis. The new domain has been linked with existing ontologies, such as Units of Measurements Ontology, Food, Nutrient and the Bibliographic Ontology. To show how such an ontology can be used in practise, it was populated with 20 isotopic measurements of Slovenian food samples. Describing data in this way offers a powerful technique for organizing and sharing stable isotope data across Food Science.
View details for DOI 10.1016/j.foodchem.2018.10.118
View details for Web of Science ID 000451430800046
View details for PubMedID 30502161
Identification of Requirements for Computer-Supported Matching of Food Consumption Data with Food Composition Data
2018; 10 (4)
This paper identifies the requirements for computer-supported food matching, in order to address not only national and European but also international current related needs and represents an integrated research contribution of the FP7 EuroDISH project. The available classification and coding systems and the specific problems of food matching are summarized and a new concept for food matching based on optimization methods and machine-based learning is proposed. To illustrate and test this concept, a study has been conducted in four European countries (i.e., Germany, The Netherlands, Italy and the UK) using different classification and coding systems. This real case study enabled us to evaluate the new food matching concept and provide further recommendations for future work. In the first stage of the study, we prepared subsets of food consumption data described and classified using different systems, that had already been manually matched with national food composition data. Once the food matching algorithm was trained using this data, testing was performed on another subset of food consumption data. Experts from different countries validated food matching between consumption and composition data by selecting best matches from the options given by the matching algorithm without seeing the result of the previously made manual match. The evaluation of study results stressed the importance of the role and quality of the food composition database as compared to the selected classification and/or coding systems and the need to continue compiling national food composition data as eating habits and national dishes still vary between countries. Although some countries managed to collect extensive sets of food consumption data, these cannot be easily matched with food composition data if either food consumption or food composition data are not properly classified and described using any classification and coding systems. The study also showed that the level of human expertise played an important role, at least in the training stage. Both sets of data require continuous development to improve their quality in dietary assessment.
View details for DOI 10.3390/nu10040433
View details for Web of Science ID 000435182900049
View details for PubMedID 29601516
View details for PubMedCentralID PMC5946218
Mixed deep learning and natural language processing method for fake-food image recognition and standardization to help automated dietary assessment.
Public health nutrition
The present study tested the combination of an established and a validated food-choice research method (the 'fake food buffet') with a new food-matching technology to automate the data collection and analysis.The methodology combines fake-food image recognition using deep learning and food matching and standardization based on natural language processing. The former is specific because it uses a single deep learning network to perform both the segmentation and the classification at the pixel level of the image. To assess its performance, measures based on the standard pixel accuracy and Intersection over Union were applied. Food matching firstly describes each of the recognized food items in the image and then matches the food items with their compositional data, considering both their food names and their descriptors.The final accuracy of the deep learning model trained on fake-food images acquired by 124 study participants and providing fifty-five food classes was 92·18 %, while the food matching was performed with a classification accuracy of 93 %.The present findings are a step towards automating dietary assessment and food-choice research. The methodology outperforms other approaches in pixel accuracy, and since it is the first automatic solution for recognizing the images of fake foods, the results could be used as a baseline for possible future studies. As the approach enables a semi-automatic description of recognized food items (e.g. with respect to FoodEx2), these can be linked to any food composition database that applies the same classification and description system.
View details for DOI 10.1017/S1368980018000708
View details for PubMedID 29623869
- A Novel Approach to statistical comparison of meta-heuristic stochastic optimization algorithms using deep statistics INFORMATION SCIENCES 2017; 417: 186–215
A rule-based named-entity recognition method for knowledge extraction of evidence based dietary recommendations
2017; 12 (6): e0179488
Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
View details for DOI 10.1371/journal.pone.0179488
View details for Web of Science ID 000404145100017
View details for PubMedID 28644863
View details for PubMedCentralID PMC5482438
StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2
2017; 9 (6)
The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2. It uses facets to describe food properties and aspects from various perspectives, making it easier to compare food consumption data from different sources and perform more detailed data analyses. However, both food composition data and food consumption data, which need to be linked, are lacking in FoodEx2 because the process of classification and description has to be manually performed-a process that is laborious and requires good knowledge of the system and also good knowledge of food (composition, processing, marketing, etc.). In this paper, we introduce a semi-automatic system for classifying and describing foods according to FoodEx2, which consists of three parts. The first involves a machine learning approach and classifies foods into four FoodEx2 categories, with two for single foods: raw (r) and derivatives (d), and two for composite foods: simple (s) and aggregated (c). The second uses a natural language processing approach and probability theory to describe foods. The third combines the result from the first and the second part by defining post-processing rules in order to improve the result for the classification part. We tested the system using a set of food items (from Slovenia) manually-coded according to FoodEx2. The new semi-automatic system obtained an accuracy of 89% for the classification part and 79% for the description part, or an overall result of 79% for the whole system.
View details for DOI 10.3390/nu9060542
View details for Web of Science ID 000404177100013
View details for PubMedID 28587103
View details for PubMedCentralID PMC5490521
- Finite-SNR Bounds on the Sum-Rate Capacity of Rayleigh Block-Fading Multiple-Access Channels With No A Priori CSI IEEE TRANSACTIONS ON COMMUNICATIONS 2015; 63 (10): 3621–32
- Random Access Protocols With Collision Resolution in a Noncoherent Setting IEEE WIRELESS COMMUNICATIONS LETTERS 2015; 4 (4): 445–48