Research Archives - Page 8 of 10

19.02.2022.27.10.2022.

IEEE IT2022 was a success!

EuroCC Montenegro and UDG took part in the organization of the 26th International Conference on Information Technology, IEEE IT2022. The conference was organized in a virtual setting this year. More information about the conference is available here.

EuroCC project was presented by Dr Luka Filipovic in the session dedicated to presentation of project results for the projects implemented in Montenegro and the region. Besides EuroCC itself, in this session Mr Stevan Cakic gave a presentation of AIMHiGH project that is done in the context of H2020 FF4EuroHPC experiments and EuroCC/UDG is a partner. This session took place on 16 February 2022.

Successful wrap up of the 26th IEEE IT conference IT2022

Also, the 2nd EuroCC Workshop on High-Performance Computing, High-Performance Data Analytics, and Artificial Intelligence. We had presenters from Slovenia, Croatia, Cyprus, Czechia, Netherlands , Serbia, and Montenegro. This event was organized in a hybrid setting where we had around 10-15 attendees at the UDG (EuroCC Montenegro), and over 40 attendees via Zoom. The workshop was split into 4 afternoon sessions spread over two days on 17-18 February. The workshop was attended by the representatives of Montenegrin academia and students (UDG, UCG), industry, and partners More details on the workshop will follow soon. The program agenda is available here.

Hybrid setting of the EuroCC Workshop during IEEE 2022

In four sessions dedicated to paper presentations, there was over 30 papers presented, several of which were related to AI and machine learning. Ms Zoja Scekic, a young researcher and student from UDG, presented a paper on the use of AI/ML for computer vision applications in Smart Parking.

Paper on the use of AI/ML for Parking Occupancy Detection

12.02.2022.27.10.2022.

Master Thesis: Ethics of Artificial Intelligence

Ms Jelena Tijanic, a master student at the University of Donja Gorica just defended her Master thesis titled “Ethics and Artificial Intelligence”. The thesis was done at the Master academic studies “Statistics” (EMOS) under supervision of her mentor prof. dr Milica Vukotic.

ABSTRACT – Artificial intelligence is ubiquitous and enables many of our daily routines – booking flights, driving without a driver, supports decision-making in governments and the private sector. Artificial intelligence technology delivers outstanding results in highly specialized areas such as cancer screening and building an inclusive environment for people with disabilities. They also help combat global problems such as climate change and world hunger, and help reduce poverty by optimizing economic aid. But technology also brings unprecedented new challenges. We see increased gender and ethnic bias, significant threats to privacy, dignity, dangers of mass surveillance, and increased use of unreliable law enforcement technologies.

The first part of the thesis presents the basic problems that the world is facing and why the development of artificial intelligence is a potential threat to the future of mankind. A new recommendation adopted by UNESCO member countries was presented. The second part of the thesis describes the basic concepts related to recommendation systems, the way they work, as well as their division. Here are some examples of where systems are used. Ethical problems that can be encountered during their development are described.Finally, a practical example of a movie recommendation system is described. The process of making the systems was described and the result analyzed.

Master Thesis: Ethics of Artificial Intelligence – Ms Jelena Tijanic

31.01.2022.27.10.2022.

Experimenting with Load Balancing Methods for Parallel Applications

Combined adaptive load balancing algorithm was tested on the HPC provider computing resources. Algorithm is based on domain decomposition and master-slave algorithms. Its core scheduling adaptive mechanism 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.

Experimenting with combined adaptive load balancing for parallel application

Simulations on up to 224 CPU cores proved its validity and better efficiency than standard domain decomposition and master slave algorithms. In addition, simulations have shown that there are no large losses due to the increase in the number of cores on which the simulation is performed. More information on the experiment goals and the algorithm is available in the following reference: L. Filipovic, B. Krstajic, and T. Popovic, “Combined adaptive load balancing algorithm for parallel applications”, 8th International Conference on Electrical, Electronic and Computing Engineering IcETRAN 2021.

29.01.2022.27.10.2022.

AI for the Energy Sector: Forecasting Day-Ahead Electricity Metrics with Machine Learning

The day-ahead energy market lets market participants commit to buy or sell wholesale electricity one day before the operating day, to help avoid price volatility. Forecasting day-ahead electricity prices and loads creates basis for decision making in this process. Mr. Milutin Pavićević, a young researcher from the University of Donja Gorica , explored the possibility to utilize artificial neural networks in order to improve the forecasting day-ahead electricity prices and loads based on the historical data. This was the topic of his Master thesis research work done under supervision of professor Tomo Popovic, which finally resulted in a scientific article published in MDPI journal Sensors. The paper is titled ”Forecasting Day-Ahead Electricity Metrics with Artificial Neural Networks” within the Special Issue Complex Data Processing Systems and Computing Algorithms: New Concepts and Applications.

During this research effort the researchers engaged the domain experts which provided us with generous help in obtaining datasets and understanding the problem of day-ahead consumption, spot price prediction, and the electricity market. The results show the promising efficiency of AI and machine learning for the task of short-term prediction of electricity metrics. With the support of EuroCC Montenegro, the future work will include experimenting on the HPC infrastructure and creation of an industry pilot demonstration for the energy sector.

Forecasting Day-Ahead Electricity Metrics with Machine Learning

ABSTRACT – As artificial neural network architectures grow increasingly more efficient in time-series prediction tasks, their use for day-ahead electricity price and demand prediction, a task with very specific rules and highly volatile dataset values, grows more attractive. Without a standardized way to compare the efficiency of algorithms and methods for forecasting electricity metrics, it is hard to have a good sense of the strengths and weaknesses of each approach. In this paper, we create models in several neural network architectures for predicting the electricity price on the HUPX market and electricity load in Montenegro and compare them to multiple neural network models on the same basis (using the same dataset and metrics). The results show the promising efficiency of neural networks in general for the task of short-term prediction in the field, with methods combining fully connected layers and recurrent neural or temporal convolutional layers performing the best. The feature extraction power of convolutional layers shows very promising results and recommends the further exploration of temporal convolutional networks in the field.

The paper can be accessed at the Sensors website at the following link.

13.01.2022.27.10.2022.

FF4EuroHPC: Experiment Update (AIMHiGH)

FF4EuroHPC experiment update: How to use AI and ML in agriculture sector? The idea of this experiment is to monitor the number of chickens, their weight and other parameters with the help of AI and a camera in which AI models will be integrated, and placed on poultry farms. Find more information here (based on FF4EuroHPC post on LinkedIn).

Currently developing AI prediction models for object detection and object segmentation

27.12.2021.27.10.2022.

Update on FF4EuroHPC experiment – AIMHiGH

EuroCC Monteengro and UDG are participating in the FF4EuroHPC experiment – AIMHiGH. Currently working on the ML models to be used for the development of IoT edge sensors to count the number of chickens in an image and/or video and estimate growth. The goal of the AIMHiGH experiment is to validate the use of HPC in AI/ML training and evaluate acceleration in the development of these prediction models. Learn more about the FF4EuroHPC project and experiments at the following link.

Development of computer vision for Edge-IoT in agriculture

15.11.2021.27.10.2022.

UDG / EuroCC Montenegro will participate in the IEEE TELFOR 2021 conference

Researchers from the UDG / NCC Monteengro will be presenting the paper titled “Human Activity Detection Using Deep Learning and Bracelet with Bluetooth Transmitter”, authored by S. Cakic, S. Sandi, D. Nedic, S. Krco and T. Popovic, at the upcoming IEEE TELFOR 2021 conference. The conference will be taking place on 23-24 November 2022. The paper presents the use of AI/ML algorithms to implement human activity detection based on data collected from wearable IoT device and the research is done through collaboration with DNET Labs, an innovation technology company from Novi Sad, Serbia. More info on the conference is available at the following link.

ABSTRACT – The use of artificial intelligence, machine learning, and deep learning is finding its purpose in various fields nowadays. This paper describes a study in which Internet of
Things and deep learning are used to implement human activity detection based on data collected from bracelet equipped with Bluetooth transmitter. The main focus of the study was development of a prediction model using deep learning that would help elderly people and their caretakers. Time series data about elderly people activity was collected from bracelet using a Bluetooth gateway and IoT platform, and later annotated based on the activity logs they kept in a form of diary. A neural network is trained to classify data into two groups (binary classification problem) corresponding to activity of the person wearing the
bracelet. Initial study shows promising results of the presented approach for the use in human activity detection for elderly.