News

Building 2, Room 706

Organizer:
Department of Informatics of the NBU in cooperation with
University "St. St. Cyril and Methodius" in Skopje, Republic of Northern Macedonia

Lecturer:
Dr. Petre Lameski

Deep learning algorithms (DL) have been successfully used in different scientific areas and have proven to be very reliable when doing classification or predictive analysis on the data. DL has become a standard for many industrial applications such as image recognition, image detection, etc. The application of DL for healthy aging is also becoming more and more popular in recent years however on the large scale, very few articles have been published that contain both "healthy aging" and "deep learning" according to Google Scholar. "Healthy aging at work" and "deep learning" on the other hand, yields 0 results.
The work done during this STSM is closely related to the design and creation of innovative ICT solutions that will be integrated into Smart Support Furniture and habitat environments.
The Deep learning algorithms are used for activity detection, recognition, health state estimation, aging factors, etc, which are all important part of the decision support processes for the healthcare and the healthy aging of the older population.

The seminar is part of the cooperative work between colleagues from the Department of Informatics at New Bulgarian University and the Faculty of Computer Science and Engineering of the Sts Cyril and Methodius University in Skopje, North Macedonia. More specifically the visit is part of the networking under European Commission COST Action CA 16226 SHELDON (Indoor living space improvement: Smart Habitat for the Elderly).


Petre was born in 1985 in Kavadarci, North Macedonia. In 2008 he graduated at the Faculty of Electrical Engineering and Information Technologies at the University of Sts Cyril and Methodius in Skopje with highest honors. In 2010 he finished his master studies at the same faculty with a thesis in Robotics. From September 2008 until September 2011 he worked as an assistant at the same faculty, teaching auditory and laboratory exercises. From September 2011, he worked as a research and teaching assistant at the Faculty of Computer Science and Engineering, and since 2017 as an assistant professor at the same faculty. He finished his Ph.D. thesis at the Faculty of Computer Science and Engineering in 2017 with a thesis titled: “Plant species recognition based on machine learning and image processing”. He has worked and led several national and international scientific projects and has published his work in international conferences, journals and book chapters in over 60 scientific papers. He has participated and has received awards in several international competitions. He has received a Microsoft AI for Earth grant for his work in plant and weed segmentation from images. In parallel with his academic career, he has worked with several companies on different products such as fraud detection systems, microscopic image processing, churn reason classification, body measurement from images, noise detection and removal from images, face defects detection and classification. He has also participated in the design and development of many mobile applications. His research interests include Intelligent systems, Decision support systems, Machine Learning, Deep Learning, Machine vision, Cognitive systems and Cognitive robotics, Time series data analysis, Assistive technologies, Ambient Intelligence, AI for mobile applications, etc.