Research Archives - Page 8 of 11

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)

14.06.2022.27.10.2022.

The upcoming IEEE COINS2022 Conference in Barcelona

Researchers from UDG and NCC Montenegro will participate in the upcoming IEEE COINS2022 conference in Barcelona, Spain. Two research papers will be presented at the conference presenting the research activities on the use of HPC and machine learning in agriculture and medicine. The conference is taking place 1-2 August 2022. More information on the conference is available here.

NCC Montenegro team members will present two papers on HPC/AI applications in medicine and agriculture

26.05.2022.27.10.2022.

Another NVIDIA Academic Grant: Equipment for Edge AI Classroom

NCC Montenegro researchers at UDG won another NVIDIA Academic Grant that will provide additional classroom equipment (Jetson Nano). This equipment will be used for implementing Edge AI classroom and realization of training in AI applications in IoT. The grant was awarded to prof. Tomo Popovic. Researchers participating in the Edge AI training are Stevan Cakic, MSc, Ivan Jovovic, Dejan Babic, and Zoja Scekic and it will be organized in with the support from NCC Montenegro.

Prof. Popovic awarded NVIDIA Academic Grant for Edge AI Classroom

Preparing the classroom for new ML course for Edge AI

17.05.2022.27.10.2022.

Mr Stevan Šandi awarded NVIDIA Academic Hardware Grant

Mr Stevan Šandi, a PhD candidate at UDG and NCC Montenegro team member, won NVIDIA Academic Hardware Grant for his PhD research under DASCAP project. He was awarded an advanced GPU hardware that will be used for AI and Data Science applications in agriculture domain. He will be experimenting with various computer vision prediction models. Doing experiments on a better GPU will significantly reduce the time to get all of the planned experiments done.

Mr. Stevan Šandi was awarded NVIDIA Academic Hardware Grant

21.03.2022.27.10.2022.

EuroCC featured in the Round Table on Machine Learning Organized by Montenegrin Academy of Sciences and Arts

EuroCC and AIMHiGH/FF4EuroHPC projects were featured in presentations during the round table “Deep Machine Learning”. This event was organized by the Montenegrin Academy of Sciences and Arts. The event took place on March 21, 2022. at the Rectorate building at the University of Montenegro. The participant discussed artificial intelligence and deep learning use cases and practical perspectives. Researchers from AIMHiGH project gave presentation on development of IoT Edge Computer Vision Sensors for smart farms supported by HPC and AI.

HPC and AI for Development of IoT Computer Vision Sensors for Smart Farms

EuroCC NCC Montengro was featured in the presentation

AIMHiGH project results were presented in the presentation

20.03.2022.27.10.2022.

Evaluation of trends in jobs and skill‑sets using data analytics: a Case study, by Dr Armin Alibasic et al.

Member of the EuroCC Montenegro National Competence Center team, Dr Armin Alibasic together with co-authors Himanshu Upadhyay, Mecit Can Emre Simsekler, Thomas Kurfess, Wei Lee Woon and Mohammed Atif Omar conducted research published in the paper titled “Evaluation of trends in jobs and skill ‑ sets using data analytics: a Case study” in the Journal of Big Data. The article is available at the following link.

APSTRACT – A novel data-driven approach is developed to identify trending jobs through a case study in the oil and gas industry. The proposed approach leverages a range of data analytics tools, including Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Factor Analysis and Non-Negative Matrix Factorization (NMF), to study changes in the market. Further, our approach is capable of identifying disparities between skills that are covered by the educational system, and the skills that are required in the job market. Novel data-driven approach is developed to identify trending jobs through a case study in the oil and gas industry. The proposed approach leverages a range of data analytics tools, including Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Factor Analysis and Non-Negative Matrix Factorization (NMF), to study changes in the market. Further, our approach is capable of identifying disparities between skills that are covered by the educational system, and the skills that are required in the job market.

Dr. Alibasic published an article in the Journal of Big Data

18.03.2022.27.10.2022.

A Scientific Paper on Edge AI and Face Mask Detection

A paper titled “Face Mask Detection Based on Machine Learning and Edge Computing”, authored by I. Jovovic, D. Babic, S. Cakic, T. Popovic, S. Krco, and P. Knezevic, was presented at the 2022 21th International Symposium INFOTEH-JAHORINA. The paper was presented by a young researcher Mr Ivan Jovovic, Faculty for Information Systems and Technologies, on 18 March 2022. The paper discussed the use of machine learning for face mask detection and porting of prediction models onto the edge Ai platform. The effort was supported by the EuroCC Monteengro team link.

Presentation of the paper in the virtual online session of the conference

ABSTRACT – This paper describes research effort aimed at the use of machine learning, Internet of Things, and edge computing for a use case in health, mainly the prevention of the spread of infectious diseases. The main motivation for the research was the Covid-19 pandemic and the need to improve control of the prevention measures implementation. In the study, the experimentation was focused on the use of machine learning to create and utilize prediction models for face mask detection. The prediction model is then evaluated on the various platforms with a focus on the use on various edge devices equipped with a video camera sensor. Different platforms have been tested and evaluated such as standard laptop PC, Raspberry Pi3, and Jetson Nano AI edge platform. Finally, the paper discusses a possible approach to implement a solution that would utilize the face mask detection function and lays out the path for the future research steps.