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Authors = Sharif Noor Zisad ORCID = 0000-0003-4568-359X

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15 pages, 1204 KiB  
Article
An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty
by Sharif Noor Zisad, Etu Chowdhury, Mohammad Shahadat Hossain, Raihan Ul Islam and Karl Andersson
Algorithms 2021, 14(7), 213; https://doi.org/10.3390/a14070213 - 15 Jul 2021
Cited by 23 | Viewed by 4614
Abstract
Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions [...] Read more.
Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual. Full article
(This article belongs to the Special Issue New Algorithms for Visual Data Mining)
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14 pages, 611 KiB  
Article
An Integrated Neural Network and SEIR Model to Predict COVID-19
by Sharif Noor Zisad, Mohammad Shahadat Hossain, Mohammed Sazzad Hossain and Karl Andersson
Algorithms 2021, 14(3), 94; https://doi.org/10.3390/a14030094 - 19 Mar 2021
Cited by 34 | Viewed by 7012
Abstract
A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If [...] Read more.
A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the R0-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated by showing the difference in accuracy between the integrated model and the general SEIR model. The result shows that the integrated model is more accurate than the general SEIR model while predicting the number of confirmed cases in Bangladesh. Full article
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