Gorjan Popovski

Jožef Stefan Institute | IJS · Department of Computer systems

Background In response to growing needs for the integration of heterogeneous data on food and nutrition security (FNS), and the current fragmentation of interoperability resources, the ‘FNS-Cloud project’ aims to develop a cross-domain, interoperable ‘food-cloud’ that integrates diverse FNS data. Currently, there is insufficient guidance on how to...

Background Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and mac...

Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques. In the context of numerical optimization, supervised regression approaches built on top of exploratory landscape analysis are becoming very popular. From...

Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regr...

Food is one of the main health and environmental factors in today’s society. With modernization the food supply is expanding and food-related data is increasing. This type of data comes in many different forms and making it inter-operable is one of the main requirements for using in any kind of analyses. One step towards this goal is data normaliza...

BACKGROUND Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and mac...

Personalized ranking systems — also known as recommender systems — use different big data methods, including collaborative filtering, graph random-walks, matrix factorization, and latent-factor models. With their wide use in various social-network, e-commerce, and content platforms, online platforms and developers are in need of better ways to choo...

Food is one of the main health and environmental factors in today's society. With modernization the food supply is expanding and food-related data is increasing. This type of data comes in many different forms and making it inter-operable is one of the main requirements for using in any kind of analyses. One step towards this goal is data normaliza...

Background Understanding the content of self-reported meals and online-published recipes is a basic requirement for further linking food and dietary concepts to heterogeneous health networks. Despite the huge amount of work that is done in the biomedical domain, the food and nutrition domains are relatively low-resourced. DietHub represents a step...

In recent years, a great amount of research has been done in predictive modeling in the domain of healthcare. Such research is facilitated by the existence of various biomedical vocabularies and standards which play a crucial role in understanding healthcare information. In addition, the Unified Medical Language System (UMLS) links together biomedi...

Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP. The techniques often rely on a set of features that measure some characteristics of the problem instance at hand. In the context of black-box optimization, thes...

Background The COVID-19 pandemic affects all aspects of human life including their food consumption. The changes in the food production and supply processes introduce changes to the global dietary patterns. Scope and Approach To study the COVID-19 impact on food consumption process, we have analyzed two data sets that consist of food preparation r...

To visually present the overall performance of several algorithms tested on several benchmark problems on one plot, we present a machine learning approach, called performViz. It allows one to clearly see, from a single plot, which algorithms are most suited for a given problem, the influence of each problem on the overall algorithm performance and...

When working on a new stochastic optimization algorithm, one task that should be performed is to compare its performance with those of state-of-the-art algorithms. The literature suggests that the most commonly applied approaches for comparing algorithms' performances use statistical analyses. However, to provide a more meaningful explanation about...

The performance measures and statistical techniques selected affect the conclusions we can draw on the behavior of the algorithms. For this reason, we propose more robust performance statistics for addressing statistical and practical significance, as well as investigating the exploration and exploitation powers of stochastic optimization algorithm...

As great amounts of food-related information is presented in the form of heterogeneous textual data, computer-based methods are useful to automatically extract such information. One way to do this is to utilize Named-Entity Recognition (NER) methods that are broadly used in computer science for information extraction. Despite the existence of numer...

In food and toxicology science, a huge amount of research and other data has been collected. To enable its full utilization, advanced statistical and computer methods are required. All data is related to food items, but additionally include different kinds of information. Nowadays the consumption of avocado has increased. To understand the full imp...

Many research questions from different domains involve combining different data sets in order to explore a research hypothesis. One of the main problems that arises here is that different data sets are structured with respect to different domain standards and ensuring their interoperability is a time-consuming task. In the biomedical domain, the Un...

Nowadays, the existence of several available biomedical vocabularies and standards play a crucial role in understanding health information. While there is a large number of available resources in the biomedical domain, only a limited number of resources can be utilized in the food domain. There are only a few annotated corpora with food concepts, a...

The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora related to different biomedical entities such as genes, phenotypes, drugs, diseases and chemical entit...

In the last decade, a great amount of work has been done in predictive modelling in healthcare. All this work is made possible by the existence of several available biomedical vocabularies and standards, which play a crucial role in understanding health information. Moreover, there are available systems, such as the Unified Medical Language System,...

The application of Natural Language Processing (NLP) methods and resources to biomedical textual data has received growing attention over the past years. Previously organized biomedical NLP-shared tasks (such as, for example, BioNLP Shared Tasks) are related to extracting different biomedical entities (like genes, phenotypes, drugs, diseases, chemi...