Author: nccmne

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.​

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.

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.

A paper titled “Detection of Plant Diseases Using Leaf Images and Machine Learning”, authored by A. Suljovic, S. Cakic, T. Popovic and S. Sandi, was presented at the 2022 21th International Symposium INFOTEH-JAHORINA. The paper was presented by a young researcher Ms Almira Suljovic, Faculty of Applied Sciences, on 18 March 2022 in the paper presentation session. The paper discussed the use of machine learning for detection of plant diseases that could be used in agriculture. The effort was supported by the EuroCC Monteengro team link.

ABSTRACT – Prevention and early detection of plant diseases is one of the main issues and challenges in agriculture. Farmers spend a lot of time observing and detecting diseased plants, often by looking at and analyzing plant leaves. Inadequate handling of plant disease such as late detection or the use of wrong pesticides often causes damage to crops, which causes a deterioration in the quality of food. This problem could be addressed using artificial intelligence and machine learning to detect plant diseases by processing digital images of leaves. As the leaf is the best indicator of whether the plant is healthy or not, by applying machine learning we can create predication models to detect the condition of the leaf in a shorter period of time and possibly prevent or reduce the losses. This paper describes experimenting with Detectron2 software library and Faster R-CNN neural network in order to detect the condition of the leaf. A dataset containing 6407 images was used to train the model. The original dataset has been extended by augmenting images using the RoboFlow tool. The experimentation and implementation was done using Google Colab, environment designed for cloud computing and machine learning development.