Denkovski, Daniel
Preferred name
Denkovski, Daniel
Official Name
Denkovski, Daniel
Main Affiliation
Email
danield@feit.ukim.edu.mk
15 results
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Item type:Publication, Delay Minimization of Federated Learning Over Wireless Powered Communication Networks(Institute of Electrical and Electronics Engineers (IEEE), 2024-01); ; Gjoreski, Hristijan - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Prediction of Hospital Readmission using Federated Learning(IEEE, 2023-06-27) - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Federated Learning for Network Intrusion Detection in Ambient Assisted Living Environments(Institute of Electrical and Electronics Engineers (IEEE), 2023-07); Given the Internet of Things’ rapid expansion and widespread adoption, it is of great concern to establish secure interaction between devices without worsening the quality of their performance. The use of machine learning techniques has been shown to improve detection of anomalous behavior in these types of networks, but their implementation leads to poor performance and compromised privacy. To better address these shortcomings, federated learning (FL) has been introduced. FL enables devices to collaboratively train and evaluate a shared model while keeping personal data on site (e.g., smart homes, intensive care units, hospitals, and so on), thus minimizing the possibility of an attack and fostering real-time distribution of models and learning. This article investigates the performance of FL in comparison to deep learning (DL) with respect to network intrusion detection in ambient assisted living environments. The results demonstrate comparable performances of FL with DL while achieving improved data privacy and security. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Power optimization of LTE-800 and coexistence with DVB-T services(Elsevier BV, 2018-08); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generic Multiuser Coordinated Beamforming for Underlay Spectrum Sharing(Institute of Electrical and Electronics Engineers (IEEE), 2016-06); ; ; Mahonen, Petri - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Survey of Bias in Healthcare: Pitfalls of Using Biased Datasets and Applications(Springer, Cham, 2023-07-09); ; Osmani, VenetArtificial intelligence (AI) is widely used in medical applications to support outcome prediction and treatment optimisation based on collected patient data. With the increasing use of AI in medical applications, there is a need to identify and address potential sources of bias that may lead to unfair decisions. There have been many reported cases of bias in healthcare professionals, medical equipment, medical datasets, and actively used medical applications. These cases have severely impacted the quality of patients’ healthcare, and despite awareness campaigns, bias has persisted or in certain cases even exacerbated. In this paper, we survey reported cases of different forms of bias in medical practice, medical technology, medical datasets, and medical applications, and analyse the impact these reports have in the access and quality of care provided for certain patient groups. In the end, we discuss possible pitfalls of using biased datasets and applications, and thus, provide the reasoning behind the need for robust and equitable medical technologies. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Cloud based solution for vital signs tracking(IEEE, 2017-07); ; ; op den Akker, Harm - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Federated Learning for Activity Recognition: A System Level Perspective(IEEE, 2023-04);