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Limited By:	Author = Nikolov, Nikola S.;

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A GA-inspired approach to the reduction of edge crossings in force-directed layouts (2016)

Ghassemi Toosi, Farshad; Nikolov, Nikola S.; Eaton, Malachy

A GA-inspired approach to the reduction of edge crossings in force-directed layouts (2016)

Ghassemi Toosi, Farshad; Nikolov, Nikola S.; Eaton, Malachy

Abstract:

We report on our findings using a genetic algorithm (GA) as a preprocessing step for force-directed graph drawings to find a smart initial vertex layout (instead of a random initial layout) to decrease the number of edge crossings in the graph. We demonstrate that the initial layouts found by our GA improve the chances of finding better results in terms of the number of edge crossings, especially for sparse graphs and star-shaped graphs. In particular we demonstrate a reduction in edge-crossings for the class of star-shaped graphs by using our GA over random vertex placement in the order of 3:1.
ACCEPTED
peer-reviewed

http://hdl.handle.net/10344/5395

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A hybrid service recommendation prototype adapted for the UCWW: a smart-city orientation (2017)

Zhang, Haiyang; Ganchev, Ivan; Nikolov, Nikola S.; Ji, Zhanlin; Ó'Droma, Máirtín

A hybrid service recommendation prototype adapted for the UCWW: a smart-city orientation (2017)

Zhang, Haiyang; Ganchev, Ivan; Nikolov, Nikola S.; Ji, Zhanlin; Ó'Droma, Máirtín

Abstract:

With the development of ubiquitous computing, recommendation systems have become essential tools in assisting users in discovering services they would find interesting. This process is highly dynamic with an increasing number of services, distributed over networks, bringing the problems of cold start and sparsity for service recommendation to a new level. To alleviate these problems, this paper proposes a hybrid service recommendation prototype utilizing user and item side information, which naturally constitute a heterogeneous information network (HIN) for use in the emerging ubiquitous consumer wireless world (UCWW) wireless communication environment that offers a consumer-centric and network-independent service operation model and allows the accomplishment of a broad range of smart-city scenarios, aiming at providing consumers with the "best" service instances that match their dynamic, contextualized, and personalized requirements and expectations. A layered architectur...

http://hdl.handle.net/10344/6262

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Applying ant colony optimization metaheuristic to the DAG layering problem (2007)

Andreev, Radoslav; Healy, Patrick; Nikolov, Nikola S.

Applying ant colony optimization metaheuristic to the DAG layering problem (2007)

Andreev, Radoslav; Healy, Patrick; Nikolov, Nikola S.

Abstract:

This paper 1 presents the design and implementation of an Ant Colony Optimization based algorithm for solving the DAG Layering Problem. This algorithm produces compact layerings by minimising their width and height. Importantly it takes into account the contribution of dummy vertices to the width of the resulting layering.

http://hdl.handle.net/10344/2362

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Dataset construction for the detection of anti-social behaviour in online communication in arabic (2018)

Alakrot, Azalden; Murray, Liam; Nikolov, Nikola S.

Dataset construction for the detection of anti-social behaviour in online communication in arabic (2018)

Alakrot, Azalden; Murray, Liam; Nikolov, Nikola S.

Abstract:

Warning: this paper contains a range of words which may cause offence. In recent years, many studies target anti-social behaviour such as offensive language and cyberbullying in online communication. Typically, these studies collect data from various reachable sources, the majority of the datasets being in English. However, to the best of our knowledge, there is no dataset collected from the YouTube platform targeting Arabic text and overall there are only a few datasets of Arabic text, collected from other social platforms for the purpose of offensive language detection. Therefore, in this paper we contribute to this field by presenting a dataset of YouTube comments in Arabic, specifically designed to be used for the detection of offensive language in a machine learning scenario. Our dataset contains a range of offensive language and flaming in the form of YouTube comments. We document the labelling process we have conducted, taking into account the difference in the Arab dialects ...

http://hdl.handle.net/10344/7878

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Feature selection methods and genomic big data: a systematic review (2019)

Tadist, Khawla; Najah, Said; Nikolov, Nikola S.; Mrabti, Fatiha; Zahi, Azeddine

Feature selection methods and genomic big data: a systematic review (2019)

Tadist, Khawla; Najah, Said; Nikolov, Nikola S.; Mrabti, Fatiha; Zahi, Azeddine

Abstract:

In the era of accelerating growth of genomic data, feature-selection techniques are believed to become a game changer that can help substantially reduce the complexity of the data, thus making it easier to analyze and translate it into useful information. It is expected that within the next decade, researchers will head towards analyzing the genomes of all living creatures making genomics the main generator of data. Feature selection techniques are believed to become a game changer that can help substantially reduce the complexity of genomic data, thus making it easier to analyze it and translating it into useful information. With the absence of a thorough investigation of the field, it is almost impossible for researchers to get an idea of how their work relates to existing studies as well as how it contributes to the research community. In this paper, we present a systematic and structured literature review of the feature-selection techniques used in studies related to big genomic...

http://hdl.handle.net/10344/8052

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Towards accurate detection of offensive language in online communication in Arabic (2018)

Alakrot, Azalden; Murray, Liam; Nikolov, Nikola S.

Towards accurate detection of offensive language in online communication in Arabic (2018)

Alakrot, Azalden; Murray, Liam; Nikolov, Nikola S.

Abstract:

We present the results of predictive modelling for the detection of anti-social behaviour in online communication in Arabic, such as comments which contain obscene or offensive words and phrases. We collected and labelled a large dataset of YouTube comments in Arabic which contains a broad range of both offensive and inoffensive comments. We used this dataset to train a Support Vector Machine classifier and experimented with combinations of word-level features, N-gram features and a variety of pre-processing techniques. We summarise the pre-processing steps and features that allow training a classifier which is more precise, with 90.05% accuracy, than classifiers reported by previous studies on Arabic text.

http://hdl.handle.net/10344/7877

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Trustworthy health-related tweets on social media in Saudi Arabia: tweet metadata analysis (2019)

Albalawi, Yahya; Nikolov, Nikola S.; Buckley, Jim

Trustworthy health-related tweets on social media in Saudi Arabia: tweet metadata analysis (2019)

Albalawi, Yahya; Nikolov, Nikola S.; Buckley, Jim

Abstract:

Background: Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. Objective: The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. Methods: A health lexicon was used to extract health-related tweets using the Twitter application programming ...

http://hdl.handle.net/10344/8173

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Weighted Item ranking for pairwise matrix factorization (2019)

Zhang, Haiyang; Ganchev, Ivan; Nikolov, Nikola S.; Ó'Droma, Máirtín

Weighted Item ranking for pairwise matrix factorization (2019)

Zhang, Haiyang; Ganchev, Ivan; Nikolov, Nikola S.; Ó'Droma, Máirtín

Abstract:

Recommendation systems employed on the Internet aim to serve users by recommending items which will likely be of interest to them. The recommendation problem could be cast as either a rating estimation problem which aims to predict as accurately as possible for a user the rating values of items which are yet unrated by that user, or as a ranking problem which aims to find the top-k ranked items that would be of most interest to a user, which s/he has not ranked yet. In contexts where explicit item ratings of other users may not be available, the ranking prediction could be more important than the rating prediction. Most of the existing ranking-based prediction approaches consider items as having equal weights which is not always the case. Different weights of items could be regarded as a reflection of items’ importance, or desirability, to users. In this paper, we propose to integrate variable item weights with a ranking-based matrix factorization model, where learning is driven by ...

http://hdl.handle.net/10344/7740