Zdravevski, Eftim

Preferred name

Zdravevski, Eftim

Official Name

Zdravevski, Eftim

Alternative Name

Eftim Zdravevski

Main Affiliation

eftim.zdravevski@finki.ukim.mk

zeftim@gmail.com

eftim@finki.ukim.mk

Scopus Author ID

0000-0001-7664-0168

39362601000

Now showing 1 - 10 of 191

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A study on appropriate segment length for generalized cuff-less blood pressure estimation from ECG features
(IEEE, 2024-05-20)
Kuzmanov, Ivan
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Lamenski, Petre
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Madevska Bogdanova, Ana
Blood pressure (BP) refers to the pressure exerted on the blood vessels as blood travels through the body. Our ultimate goal is to build a stable model for BP estimation as part of a triage process. In this study, we experiment to determine a suitable signal segment only from electrocardiogram (ECG) signals, to ensure a fast and reliable process of the BP estimation. The used dataset contains only high-quality ECG and arterial blood pressure (ABP) signals extracted from the Medical Information Mart for Intensive Care, MIMIC II and MIMIC III databases by our methodology. It was processed three times using similar machine learning (ML) methodologies, with different segment lengths. Three different datasets are generated using a non-overlapping window with a size of 8, 15, and 30 seconds, with the same ECG features. Several linear and nonlinear Machine Learning models are built on these datasets, and their results are compared. Our best results were obtained by a light gradient-boosting machine (LightGBM) regression model trained on the 30-second dataset. The model achieves Mean Absolute Error (MAE) of 10.87, 6.55, and 7.29, and Root Mean Squared Error (RMSE) of 14.49, 8.68, and 9.68 for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP), respectively. The results of our experiment indicate that a duration of 30 seconds is the minimum length that provides informative features, fulfilling the need for real-time delivery.
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Automating Feature Extraction from Entity-Relation Models: Experimental Evaluation of Machine Learning Methods for Relational Learning
(MDPI AG, 2024-04-01)
Stanoev, Boris
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Mitrov, Goran
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<jats:p>With the exponential growth of data, extracting actionable insights becomes resource-intensive. In many organizations, normalized relational databases store a significant portion of this data, where tables are interconnected through some relations. This paper explores relational learning, which involves joining and merging database tables, often normalized in the third normal form. The subsequent processing includes extracting features and utilizing them in machine learning (ML) models. In this paper, we experiment with the propositionalization algorithm (i.e., Wordification) for feature engineering. Next, we compare the algorithms PropDRM and PropStar, which are designed explicitly for multi-relational data mining, to traditional machine learning algorithms. Based on the performed experiments, we concluded that Gradient Boost, compared to PropDRM, achieves similar performance (F1 score, accuracy, and AUC) on multiple datasets. PropStar consistently underperformed on some datasets while being comparable to the other algorithms on others. In summary, the propositionalization algorithm for feature extraction makes it feasible to apply traditional ML algorithms for relational learning directly. In contrast, approaches tailored specifically for relational learning still face challenges in scalability, interpretability, and efficiency. These findings have a practical impact that can help speed up the adoption of machine learning in business contexts where data is stored in relational format without requiring domain-specific feature extraction.</jats:p>
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Advancing methods in big data capture, integration, classification and liberation
(BioMed Central, 2023-04-27)
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Pires, Ivan Miguel
This special issue focuses on the importance of advancing research techniques for managing and analyzing data in today’s data-rich landscape. In this editorial, we set the context and invite contributions for a BMC Collection of articles titled ‘Advancing methods in data capture, integration, classification and liberation’. The collection emphasizes the need for efficient ways to standardize, cleanse, integrate, enrich, and liberate data, highlighting recent advancements in research methods and industrial technologies that facilitate this. We invite researchers to submit their best work to the collection and to showcase the latest advancements and additions to research techniques.
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Temporal Authorization Graphs: Pros, Cons and Limits
(Springer International Publishing, 2022-01)
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Popovski, Ognen
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As more private data is entering the web, defining authorization about its access is crucial for privacy protection. This paper proposes a policy language that leverages SPARQL expressiveness and popularity for flexible access control management and enforces the protection using temporal graphs. The temporal graphs are created during the authentication phase and are cached for further usage. They enable design-time policy testing and debugging, which is necessary for correctness guarantee. The security never comes with convenience, and this paper examines the environments in which the temporal graphs are suitable. Based on the evaluation results, an approximated function is defined for suitability determination based on the expected temporal graph size.
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Smart Objects and Technologies for Social Goods
(Springer Nature, 2023-03-15)
Pires, Ivan Miguel
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Garcia, Nuno Cruz
This book constitutes the refereed post-conference proceedings of the 8th EAI International Conference on Smart Objects and Technologies for social Goods, GOODTECHS 2022, held in Aveiro, Portugal, in November 16-18, 2022 The 7 full papers presented were selected from 18 submissions and issue design, implementation, deployment, operation, and evaluation of smart objects and technologies for social good. Social goods are products and services provided through private enterprises, government, or non-profit institutions and are related to healthcare, safety, sports, environment, democracy, computer science, and human rights.
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Air pollution data: A dataset gathered through a crowd sensing platform
(2015-01-14)
Temkov, Slave
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Chavkovski, Panche
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This is a dataset on air pollution monitoring sourced from a crowd-sensing IoT platform. The dataset includes real-time data on various pollutants, including PM2.5, PM10, and NO2 levels, along with atmospheric data such as humidity and temperature. This data is collected across multiple urban locations in Skopje, North Macedonia.
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Technological Solutions for Older People with Alzheimer's Disease: Review
(Bentham Science Publishers Ltd., 2018-04-27)
Maresova, Petra
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Tomsone, Signe
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Madureira, Joana
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Mendes, Ana
In the nineties, numerous studies began to highlight the problem of the increasing number of people with Alzheimer's disease in developed countries, especially in the context of demographic progress. At the same time, the 21st century is typical of the development of advanced technologies that penetrate all areas of human life. Digital devices, sensors, and intelligent applications are tools that can help seniors and allow better communication and control of their caregivers. The aim of the paper is to provide an up-to-date summary of the use of technological solutions for improving health and safety for people with Alzheimer's disease. Firstly, the problems and needs of senior citizens with Alzheimer's disease (AD) and their caregivers are specified. Secondly, a scoping review is performed regarding the technological solutions suggested to assist this specific group of patients. Works obtained from the following libraries used in this scoping review: Web of Science, PubMed, Springer, ACM and IEEE Xplore. Four independent reviewers screened the identified records and selected relevant articles which were published in the period from 2007 to 2018. A total of 6,705 publications were selected. In all, 128 full papers were screened. Results obtained from the relevant studies were furthermore divided into the following categories according to the type and use of technologies: devices, processing, and activity recognition. The leading technological solution in the category of devices are wearables and ambient non-invasive sensors. The introduction and utilization of these technologies however brings about challenges in acceptability, durability, ease of use, communication, and power requirements. Furthermore, in needs to be pointed out that these technological solutions should be based on open standards.
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Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution
(Public Library of Science (PLoS), 2024)
Kostadinov, Martin
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Coelho, Paulo Jorge
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Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution.
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IN THE FINE PRINT: INVESTIGATING EDTECH PROVIDERS’DATA PRIVACY COMMITMENT-TOOLS FOR SCHOOLS
(IATED, 2024)
Hillman, V
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Barud, K
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Henne, T
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Saillant, C
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Radkoff, E
In the evolving educational technology (edtech) landscape, quality assessment processes are integral to education governance and ensuring quality at all educational levels. Transparency in data processing provided to users and adherence to privacy laws by edtech providers have become critical concerns for building trust with education stakeholders. This study explores data protection practices of selected edtech providers using an innovative mixed-method approach combining manual assessments with Machine Learning techniques. Our research focuses on: (1) empirical analysis of the transparency and legality of the information shared with schools by providers based on the articulation of their data privacy policies (DPPs) (2) methodological exploration integrating human and ML-based analyses. These components scrutinize how edtech providers communicate their data processing practices to schools and comply with privacy regulations such as the General Data Protection Regulation (GDPR) and age-appropriate standards, outlined in their DPPs. These practices are crucial for building trust between schools and edtech providers and for updating relevant government policies that address the challenges of digitizing education (evidencing recent unethical and illegal data practices). Our motivation stems from the statutory requirements schools must meet to ensure they integrate quality edtech products into their operations. Conducting Data Privacy Impact Assessments and evaluating providers’ DPPs, as part of procurement, while protecting students’ basic rights, is costly, labor-intensive and requires expertise beyond pedagogy. Hence, our research focuses on seeking to develop a non-expert template that can streamline the initial assessments of DPPs and evaluate a provider’s transparency towards users; and, test innovative technologies to scale this demanding process effectively and efficiently. Initial findings were derived from the ML-supported assessment of 10 popular edtech providers’ DPPs. These findings highlight varying degrees of transparency and compliance with data protection requirements concerning data processing information for end-users. They also elucidate whether current ML techniques such as OpenAI’s chatGPT ensure reliable automated assessments or produce untrustworthy results. Our methodology evaluates the clarity and comprehensibility of DPPs through manual scrutiny and leverages ML techniques for analysis of large datasets. It identifies current errors associated with ML applications in this context. This dual approach enhances the robustness and scalability of our evaluation framework, offering insights on how future assessments of edtech could be standardized and automated. The study contributes to discussions at the intersection of education, technology, ethics, policy and governance, offering actionable insights for education stakeholders in navigating the complexities of data privacy regulation and promoting responsible edtech innovation. Our findings and methodology contribute to global discourse in education and research by addressing the datafication of education and the application of AI in legal and ethical assessment practices. We advocate for ethical edtech that not only enhances educational outcomes but also prioritizes transparency, legality, and ethical integrity, with assessment of ML tools which could support and facilitate schools’ procurement and assessment processes.
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Methods for urban Air Pollution measurement and forecasting: Challenges, opportunities, and solutions
(MDPI, 2023-09-15)
Mitreska Jovanovska, Elena
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Batz, Victoria
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Herzog, Michael A
In today’s urban environments, accurately measuring and forecasting air pollution is crucial for combating the effects of pollution. Machine learning (ML) is now a go-to method for making detailed predictions about air pollution levels in cities. In this study, we dive into how air pollution in urban settings is measured and predicted. Using the PRISMA methodology, we chose relevant studies from well-known databases such as PubMed, Springer, IEEE, MDPI, and Elsevier. We then looked closely at these papers to see how they use ML algorithms, models, and statistical approaches to measure and predict common urban air pollutants. After a detailed review, we narrowed our selection to 30 papers that fit our research goals best. We share our findings through a thorough comparison of these papers, shedding light on the most frequently predicted air pollutants, the ML models chosen for these predictions, and which ones work best for determining city air quality. We also take a look at Skopje, North Macedonia’s capital, as an example of a city still working on its air pollution measuring and prediction systems. In conclusion, there are solid methods out there for air pollution measurement and prediction. Technological hurdles are no longer a major obstacle, meaning decision-makers have ready-to-use solutions to help tackle the issue of air pollution.

Zdravevski, Eftim

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