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Graph modelling for tracking the COVID-19 pandemic spread

Infectious Disease Modelling 2021年第1期6卷 112-122页

作者： Rasim Alguliyev ramiz aliguliyev Farhad Yusifov Institute of Information Technology Azerbaijan National Academy of SciencesBakuAzerbaijan

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious ***,the modelling of a complex system such as the spread of COVID-19 infection is among the topical *** aim of this article is graphbased modelling of the COVID-19 infection *** article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main *** propose a conceptual model of COVID-19 epidemic by considering the social distance,the duration of contact with an infected person and their location-based demographic *** on the hypothetical scenario of the spread of the virus,a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of *** application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical *** advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the *** approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty *** that social,economic,demographic factors,the population density,mental values and *** the increase in number of cases of infection and hence,the research was not able to consider all *** future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.

关键词： COVID-19 Pandemic Modelling Epidemiological characteristics Graph visualization

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Efficient algorithm for big data clustering on single machine

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CAAI Transactions on Intelligence Technology 2020年第1期5卷 9-14页

作者： Rasim M.Alguliyev ramiz M.aliguliyev Lyudmila V.Sukhostat Institute of Information Technology Azerbaijan National Academy of Sciences9AB.Vahabzade StreetAZ1141 BakuAzerbaijan

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, itbecame necessary to develop new clustering algorithms capable of such data processing. This study proposes a newparallel clustering algorithm based on the k-means algorithm. It significantly reduces the exponential growth ofcomputations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initialdataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for eachbatch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real largedatasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposedapproach is compared with k-means and its modification. The experiments show that the proposed algorithm is apromising tool for clustering large datasets in comparison with the k-means algorithm.

关键词： algorithm. algorithm batch

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Evolutionary Algorithm for Extractive Text Summarization

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Intelligent Information Management 2009年第2期1卷 128-138页

作者： Rasim ALGULIEV ramiz aliguliyev 不详

Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.

关键词： sentence clustering document summarization discrete differential evolution algorithm

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Multi-Document Summarization Model Based on Integer Linear Programming

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Intelligent Control and Automation 2010年第2期1卷 105-111页

作者： Rasim Alguliev ramiz aliguliyev Makrufa Hajirahimova Institute of Information Technology of National Academy of Sciences of Azerbaijan

This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated sum- maries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.

关键词： Multi-Document Summarization Content Coverage Less Redundancy Integer Linear Programming

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Extracting a Heterogeneous Social Network of AcademicResearchers on the Web Based on Information Retrieved from Multiple Sources

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American Journal of Operations Research 2011年第2期1卷 33-38页

作者： Rasim M. Alguliev ramiz M aliguliyev Fadai S Ganjaliyev Institute of Information Technology Azerbaijan National Academy of Sciences Baku Azerbaijan

The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions. Hence, social network of academic researchers can be of important value for scientific community. This information can be retrieved from various data source currently available on the Web. From each of them a separate net-work can be built. In this paper we present a method which can be used to combine multiple single-relational networks into a single network which will combine all relations, hence it will be multi-relational.

关键词： Multi-Relational Networks Academic Researchers’ Network Data Source Criteria

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Aggregating Edge Weights in Social Networks on the Web Extracted from Multiple Sources with Different Importance Degrees

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Journal of Intelligent Learning Systems and Applications 2012年第2期4卷 154-158页

作者： Rasim M. Alguliev ramiz M. aliguliyev Fadai S. Ganjaliyev Institute of Information Technology Azerbaijan National Academy of Sciences Baku Azerbaijan Institute of Information Technology Azerbaijan National Academy of Sciences Baku Azerbaijan.

Information on a given set of entities can be derived from multiple sources on the Web. Social networks built from these sources, using these entities as nodes, will have different edge weight values, although the entities will be the same. If these sources are different, one will not normally trust each of them equally. One source will be considered more or less importance than the other. Completely ignoring sources with little importance may yield unexpected results. In this paper, we propose a method for aggregating weight values for social networks built from the Web using different sources. First, multiple social networks are built from different data sources. Then the received edge weights are aggregated, with the importance of a data source taken into account.

关键词： OWA Operator Orness Data Source

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