Hybrid machine learning approach for community and overlapping community detection in social network

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future

Advances in Production Management Systems. Smart Manufacturing for Industry 4.0 - IFIP Advances in Information and Communication Technology ◽

10.1007/978-3-319-99707-0_39 ◽

2018 ◽

pp. 311-317 ◽

Cited By ~ 4

Author(s):

Sangje Cho ◽

Gökan May ◽

Ioannis Tourkogiorgis ◽

Roberto Perez ◽

Oscar Lazaro ◽

...

Keyword(s):

Machine Learning ◽

Predictive Maintenance ◽

Learning Approach ◽

Machine Learning Approach ◽

The Future ◽

Hybrid Machine ◽

Smart Factories ◽

Factories Of The Future

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Aspects of Various Community Detection Algorithms in Social Network Analysis

Advanced Methodologies and Technologies in Media and Communications - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-7601-3.ch026 ◽

2019 ◽

pp. 327-341

Author(s):

Nicole Belinda Dillen ◽

Aruna Chakraborty

Keyword(s):

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Community Detection ◽

Overlapping Community Detection ◽

Detection Algorithms ◽

Novel Approach ◽

Recent Developments ◽

Overlapping Community ◽

Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

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