Faculty of Electrical Engineering and Information Technologies

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/10

Browse

Settings

Has files: NoSubject: search.filters.subject.Machine Learning

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Network Traffic Analysis and Control by Application of Machine Learning
    (Springer, Cham, 2023-07-09)
    Bogoevski, Zlate
    ;
    Jovanovski, Ivan
    ;
    Velichkovska, Bojana
    ;
    Efnusheva, Danijela
    The purpose of this paper is to analyses how networks work, how data is transmitted, what information we get from each router during data transmission, getting to know the basics of machine learning and how to create models that will learn how networks work. By applying machine learning methods, results are obtained that show us the shape of a network. With different methods we can get information about how we can plan the network, in terms of expanding the network if the capacity of the links is almost full or when one of the links has predispositions to go from an active state to an inactive one. The results show satisfactory outcomes through the use of three different machine learning models that were capable of accurately detecting the functionality of a port, calculating its utilization and learning when the utilization hits a threshold of above 75%.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Analysis of Early Cancer Diagnosis Using Machine Learning
    (Springer, Cham, 2024)
    Gjosheva, Marija
    ;
    Bogoevski, Zlate
    ;
    Velichkovska, Bojana
    ;
    Efnusheva, Danijela
    ;
    Cancer is a group of diseases with similar symptoms, all involving uncontrolled growth and reproduction of cells. With around 8 million deaths each year, it is the second leading cause of death worldwide in developing countries and the first in the developed world. In contemporary medicine, early cancer diagnosis for every known type is essential. Machine learning has the potential to completely transform the process and increase the number of lives saved. In order to make predictions, computers develop complex data models and search for patterns. Early cancer diagnosis could undergo a revolution because of machine learning. This research’s goal is to outline the issue surrounding cancer diagnoses in patients and all the difficulties they experience. A suitable strategy will be to model the risk of cancer and patient outcomes given the growing trend of employing machine learning technics in cancer research. A specific model has been developed that, if applied appropriately, can reduce the number of lost lives and, at the same time, increase the number of individuals capable of coping with this disease. The results indicate that the created model can be used by professionals to identify lung cancer with efficiency. If the prediction is accurate, the doctor may be able to develop a better treatment plan and provide the patient with an early diagnosis. The study's findings show that the number of patients has been rising recently, yet early detection is crucial because it can help avert serious complications.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Mobile Edge Computing services with QoS support model for Next Generation Mobile Networks
    (FEEIT, Skopje, 2022)
    Shuminoski, Tomislav
    ;
    Velichkovska, Bojana
    ;
    This paper presents a novel overview in intelligent multi-access QoS mobile edge computing (MEC) for beyond 5G networks and services. There are many challenges faced by the expansion of Cloud networks and Mobile Networks, which can be solved by providing connectivity at the edge of the network, i.e. with Mobile Edge computing networks. The MEC improves overall network performance and reduces end-to-end service delay. Also, the improved advanced QoS model including Machine Learning (ML) algorithm within for next generation of mobile networks and services are proposed. The purpose of the ML algorithm is to understand the traffic activity and determine how the traffic schedule should be made. Given a set of machines and a set of jobs, the model should compute the processing schedule that minimizes specified metrics. The proposed model combines the most powerful features of both Cloud and Edge computing, independent from any existing and future Radio Access Technology, leading to possible better performance utility networks, lower service delay with high QoS provisioning for many used multimedia service. Finally, this paper gives an overview of the existing Mobile Edge Computing technologies and several existing use cases. Undoubtedly, MEC with QoS support is an innovative network paradigm going in 6G, which can essentially answer many of the existing Mobile Networks’ challenges.