Publications Archives - Page 6 of 6

12.09.2021.27.10.2022.

UDG / EuroCC Montenegro will take part at the IEEE IDAACS 2021 conference

UDG researchers will be presenting the paper titled “Forecasting Day-Ahead Electricity Price with Artificial Neural Networks: a Comparison of Architectures”, authored by M. Pavicevic and T. Popovic, at the upcoming IEEE IDAACS 2021 conference. The conference will be taking place on 22-25 September 2021. The paper presents the use of AI/ML algorithms and CNN architectures for prediction of prices for the electric energy markets. More info on the conference is available at the following link.

ABSTRACT – The spot price prediction for the electric energy markets is a widely approached problem, used by many participants in the market. The ever-shifting rules and regulations, rising percentage of the electricity on the market being produced by solar and wind plants and many stochastic factors influencing it make the market price of electricity very volatile and hard to forecast. Many methods are used to tackle this problem, and their efficiency varies from dataset to dataset. In this work, we use the dataset of hourly day-ahead spot prices from the Hungarian HUPX market, and couple it with weather data for Hungary. We test various types of Dense, Recurrent and Convolutional neural network architectures and report on the results.

M. Pavicevic, a Master candidate at the UDG, will be presenting at the IEEE IDAACS 2021

29.08.2021.27.10.2022.

UDG researchers will be presenting a paper at IEEE IcETRAN 2021 Conference

Members of our EuroCC Montenegro team will be presenting the paper titled “Combined adaptive load balancing algorithm for parallel applications”, authored by L. Filipovic, B. Krstajic, and T. Popovic, at the upcoming IcETRAN 2021 conference. The conference will be taking place on Sep 8-10, 2021. More info on the conference is available at the following link.

ABSTRACT – Development and improvement of efficient techniques for parallel task scheduling on multiple cores processors is one of the key issues encountered in parallel and distributed computer systems. The purpose of process distribution improvement in parallel applications is in increased system performance, reduced application execution time, reduced losses and increased resource utilization.

This paper presents combined adaptive load balancing algorithm based on domain decomposition and master-slave
algorithms and its core scheduling adaptive mechanism that handles load redistribution according obtained and analyzed data. Selection of distribution algorithm, based on collected parameters and previously defined conditions, proved to deliver increased performances and reduced imbalance. Results of simulations confirm better performance of proposed algorithms compared to the standard algorithms reviewed in this paper.

Dr. L. Filipovic will be presenting the paper at the IcETRAN 2021, Sep 8-10, 2021

17.02.2021.27.10.2022.

Conference paper on AI application in medicine

A paper titled “Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network” authored by L. Racic, T. Popovic. S. Cakic, S. Sandi was presented at the 25th IEEE Conference on Information Technology. The paper is available in the IEEE Xplore repository at the following link.

ABSTRACT – Artificial intelligence has found its use in various fields during the course of its development, especially in recent years with the enormous increase in available data. Its main task is to assist making better, faster and more reliable decisions. Artificial intelligence and machine learning are increasingly finding their application in medicine. This is especially true for medical fields that utilize various types of biomedical images and where diagnostic procedures rely on collecting and processing a large number of digital images. The application of machine learning in processing of medical images helps with consistency and boosts accuracy in reporting. This paper describes the use of machine learning algorithms to process chest X-ray images in order to support the decision- making process in determining the correct diagnosis. Specifically, the research is focused on the use of deep learning algorithm based on convolutional neural network in order to build a processing model. This model has the task to help with a classification problem that is detecting whether a chest X-ray shows changes consistent with pneumonia or not, and classifying the X-ray images in two groups depending on the detection results.

The paper was presented by mr Luka Racic

17.02.2021.27.10.2022.

Conference paper on the use of Natural Language Processing

A paper titled “Applying natural language processing to analyze customer satisfaction” authored by A. Alibasic and T. Popovic was presented at the 25th IEEE conference on Information Technology. More about the conference cab be found at the following link.

ABSTRACT – The aim of this paper is to analyze customer satisfaction by applying natural language processing (NLP). We have collected over 50,000 airline reviews from TripAdvisor data in the period from 2016 until 2019. This analysis demonstrates the capability of discovering the pain points of the customers by using data science techniques related to NLP. Our study shows that in today`s world, data-driven decisions must be taken quickly in order to maintain customer satisfaction and prevent customer churn. The paper is available at the following link.