Research Archives - Page 6 of 10

05.01.2023.19.12.2024.

HPC4S3ME IPA Project

The full title of this new project is “Building scientific and innovation potential to utilize HPC and AI for S3 Smart Specialisation in Montenegro – HPC4S3ME” and it is funded by the IPA II program, call reference EuropeAid/172-351/ID/ACT/ME.

The overall objective of HPC4S3ME project is to contribute to straightening research excellence by building scientific and innovation potential based on the use of high performance computing and artificial intelligence (AI) for applications in industrial domains proposed by the Smart Specialisation Strategy (2019-2024) for Montenegro. The implementation of this project will provide a state-of-the-art environment for young researchers to gain experience in research and development in computer science, more specifically to apply machine learning and deep learning algorithms supported by HPC in order to create innovative information-communication technology solutions for applications in agriculture and food value chain, health and tourism, energy and sustainable environment, namely the priority domains identified by the smart specialisation strategy. This is a two-year project and it starts in Jan 2023.

03.01.2023.28.01.2023.

MAIA – Montenegrin AI Association

MAIA – Montenegrin AI Association is a Non-Governmental organization, founded in September 2022 with an ambition to bring together the Montenegrin AI community. Our goal is to popularize Artificial Intelligence related research and spread awareness of its importance in our country, but also encourage our society to join the fast wave of AI innovation in the World. Several NCC Montenegro team members are taking part in this initiative. Check MAIA website for more details at the following link.

26.11.2022.06.01.2023.

A scientific paper at IEEE ACIT 2022 conference

Researchers from UDG and NCC Montenegro presented a scientific paper at the 2022 International Arab Conference on Information Technology (ACIT). The conference took place at the Al Ain University – Abu Dhabi Campus on November 22-24, 2022. The paper “Overcoming Limitations of Statistical Methods with Artificial Neural Networks” was authored by M. Grebovic, L. Filipovic, I. Katnic, M. Vukotic, and T. Popovic. More information about the conference is available here. The paper is available at IEEE Xplore at the following link.

ABSTRACT – Traditional statistical models as tools for summarizing patterns and regularities in observed data can be used for making predictions. However, statistical prediction models contain small number of important predictors, which means limited informative capability. Also, predictive statistical models that provide some type of pseudo-correct regular statistical patterns, are used without previous understanding of the used data causality. Machine Learning (ML) algorithms as area in Artificial Intelligence (AI) provide the ability to interpret and understand data in more sophisticated way. Artificial Neural Networks as kind of ML methods use non-linear algorithms, considering links and associations between parameters, while statistical use one-step-ahead linear processes to improve only short-term prediction’s accuracy by minimizing cost function. Disregarding that designing an optimal artificial neural network is very complex process, they are considered as potential solution for overcoming main flaws of statistical prediction models. However, they will not automatically improve predictions accuracy, so several artificial neural networks and traditional statistical methods are evaluated and analyzed through accuracy measures for prediction purposes in various fields of applications. Based on gained results, couple of techniques for improving artificial neural networks are proposed to get better accuracy results than statistical predictive methods.

Click on image to open link to IEEE Xplore

31.10.2022.31.10.2022.

FoodDecide – cooperation between the FoodHub Centre of Excellence and HPC NCC Montenegro

This project aims to develop effective software for decision-making support, i.e. to facilitate the business process of entities in the food business in our country, as well as competent authorities, and from the aspect of support and more effective strengthening and ensuring of food safety and research of disease outbreaks.

Researchers from the FoodHub CoE and HPC NCC Montenegro work on the FoodDecide project to identify the most important data necessary for software development such as visualisation of the food value chain.

FoodDecide – collaboration between FoodHub CoE and HPC NCC Montenegro

20.10.2022.27.10.2022.

BSc Thesis: AI/ML Computer Vision for Smart Parking

Ms Zoja Scekic, a student of the Faculty of Applied Sciences, defended her BSc Thesis in Electrical Engineering and Computer Science. The topic of the thesis work was the use of machine learning to detect and classify the parking spaces by processing images from camera sensors. Such a solution could find application in Smart city solutions. The work focused on the creation of a prediction model as well as validation with images collected at the UDG parking. She has done her thesis work under the supervision of prof. Tomo Popovic, PhD, and mr Stevan Cakic, MSc.

ABSTRACT – Smart city is one area with the growing use of Internet of Things and Artificial Intelligence. The concept of smart cities relies on making quality of life better, and solving important problems, such as global warming, public health, energy and resources. Smart parking management is one of the smart city use cases. This paper describes the use of deep learning algorithms to process images of parking lots and determine their current occupancy. The development of prediction models was done using PKLot dataset with 12417 images, Detectron2 software library, and Faster R-CNN algorithm. The resulting models can be integrated into parking space sensors and used for building smart parking solutions, and thus lead to more efficient use of space in urban areas, reduced traffic congestion, as well as reducing parking surfing to minimum.

Another excellent graduate from the Faculty of Applied Sciences

19.10.2022.27.10.2022.

AIMHIGH Project Presetented at the FF4EuroHPC Workshop on OC1 Experiments

FF4EuroHPC Experiment 1003 focused on AI/ML Based Computer Vision for Next Generation Poultry Farms was presented at the OC1 Workshop today. We discussed the project objectives, experiment approach, the benefits of the use of HPC and Deep Learning. Learn more about FF4EuroHPC project and HPC experiments at https://www.ff4eurohpc.eu/.

The fourth workshop took place on October 19th, 2022 and included presentations of the experiments related to Maintenance, Agriculture & Assets Management Sectors. University of Donja Gorica and NCC Montenegro were part of the experiment presentation related to the use of HPC to develop AI/ML computer vision solutions for smart agriculture.

AIMHIGH Presentation at FF4EuroHPC Workshop

04.08.2022.27.10.2022.

IEEE COINS 2022: HPC and Deep Learning for Computer Vision in Smart Farms

Researchers from EuroCC Montenegro presented two papers at the IEEE International Conference on Omni-Layer Intelligent Systems (COINS). IEEE COINS (link) is the right place to be. IEEE COINS brings together experts in Digital Transformation (from AI and IoT to Cloud, Blockchain, Cybersecurity, and Robotics) from around the globe. IEEE COINS includes a multi-disciplinary program from technical research papers, to panels, workshops, and tutorials on the latest technology developments and innovations addressing all important aspects of the IoT & AI ecosystem. The conference took place 1-3 August in Barcelona.

This paper was a result of the collaboration on FF4EuroHPC application experiment project called AIMHiGH that focuses on computer vision and the use of HPC to develop object detection prediction models for the use in smart agriculture, more specifically in the poultry sector. The title of the paper is “Developing Object Detection Models for Camera Applications in Smart Poultry Farms”.

ABSTRACT – This paper proposes the use of high-performance computing and deep learning to create prediction models that can be deployed as a part of smart agriculture solutions in the poultry sector. The idea is to create object detection models that can be ported onto edge devices equipped with camera sensors for the use in Internet of Things systems for poultry farms. The object detection prediction models could be used to create smart camera sensors that could evolve into sensors for counting chickens or detecting dead ones. Such camera sensor kits could become a part of digital poultry farm management systems in shortly. The paper discusses the approach to the development and selection of machine learning and computational tools needed for this process. Initial results, based on the use of Faster R-CNN network and high-performance computing are presented together with the metrics used in the evaluation process. The achieved accuracy is satisfactory and allows for easy counting of chickens. More experimentation is needed with network model selection and training configurations to increase the accuracy and make the prediction useful for developing a dead chicken detector. (link)