Kalia Orphanou

Kalia Orphanou
Open University of Cyprus · Faculty of Pure and Applied Sciences

About

17
Publications
1,486
Reads
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198
Citations
Citations since 2016
12 Research Items
192 Citations
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Introduction
Kalia Orphanou currently works at the CyCAT group at the Open University of Cyprus. Kalia does research in Data Mining, Machine Learning, Evolutionary Algorithms, Engineering and Medicine and Artificial Intelligence. Their most recent publication is 'Learning bayesian network structures with GOMEA'.

Publications

Publications (17)
Conference Paper
The unprecedented events of the COVID-19 pandemic have generated an enormous amount of information and populated the Web with new content relevant to the pandemic and its implications. Visual information such as images has been shown to be crucial in the context of scientific communication. Images are often interpreted as being closer to the truth...
Article
The rapid proliferation of misinformation and disinformation on the Internet has brought dire consequences upon societies around the world, fostering extremism, undermining social cohesion and threatening the democratic process. This impact can be attested by recent events like the COVID-19 pandemic and the 2020 US presidential election. The impact...
Article
Full-text available
During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the “picture” of the Covid-19 pandemic accessed by users differs. We explore variations in what users “see” concerning the pandemic through Google image search, using a two-step approach....
Article
The first FATE Winter School, organized by the Cyprus Center for Algorithmic Transparency (CyCAT) provided a forum for both students as well as senior researchers to examine the complex topic of Fairness, Accountability, Transparency and Ethics (FATE). Through a program that included two invited keynotes, as well as sessions led by CyCAT partners a...
Preprint
Full-text available
Mitigating bias in algorithmic systems is a critical issue drawing attention across communities within the information and computer sciences. Given the complexity of the problem and the involvement of multiple stakeholders, including developers, end-users and third-parties, there is a need to understand the landscape of the sources of bias, and the...
Article
Full-text available
In recent years, the increasing propagation of hate speech in online social networks and the need for effective counter-measures have drawn significant investment from social network companies and researchers. This has resulted in the development of many web platforms and mobile applications for reporting and monitoring online hate speech incidents...
Conference Paper
Bayesian networks (BNs) are probabilistic graphical models which are widely used for knowledge representation and decision making tasks, especially in the presence of uncertainty. Finding or learning the structure of BNs from data is an NP-hard problem. Evolutionary algorithms (EAs) have been extensively used to automate the learning process. In th...
Article
In this paper, we develop a Naïve Bayes classification model integrated with temporal association rules (TARs). A temporal pattern mining algorithm is used to detect TARs by identifying the most frequent temporal relationships among the derived basic temporal abstractions (TA). We develop and compare three classifiers that use as features the most...
Conference Paper
We present a Naive Bayes classification model where the features are temporal association rules (TARs) annotated with their possible recurrence patterns, referred to as periodic TARs. To analyze clinical time series we rely on several Temporal Data Mining (TDM) methods, like temporal abstractions (TAs). We used this approach to diagnose coronary he...
Conference Paper
Coronary heart disease (CHD) is the leading cause of mortality worldwide. Primary prevention of CHD denotes limiting a first CHD event in individuals who have not been formally diagnosed with the disease. This paper demonstrates how the integration of a Dynamic Bayesian network (DBN) and temporal abstractions (TAs) can be used for assessing the ris...
Article
Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal events and their causal and temporal dependencies. Temporal abstraction (TA) is a knowledge-based process which abstracts raw temporal data into higher level interval-based concepts. In this paper, we present an extended DBN model which integrates TA me...
Article
Objectives: Temporal abstraction (TA) of clinical data aims to abstract and interpret clinical data into meaningful higher-level interval concepts. Abstracted concepts are used for diagnostic, prediction and therapy planning purposes. On the other hand, temporal Bayesian networks (TBNs) are temporal extensions of the known probabilistic graphical...
Article
Abstraction of temporal data (TA) aims to abstract time-points into higher-level interval concepts and to detect significant trends in both low-level data and abstract concepts. TA methods are used for summarizing and interpreting clinical data. Dynamic Bayesian Networks (DBNs) are temporal probabilistic graphical models which can be used to repres...

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