Decision analysis and methods
Shahla Jahangiri; Milad Abolghasemian; Adel Pourghader Chobar; Ahmadreza Nadaffard; Vahid Mottaghi
Abstract
China introduces a new strain of coronavirus as a causative of a new respiratory disease after several people contracted an unusual pneumonia in December 2019. The World Health Organization stated that the outbreak of the virus resulted in public health emergencies around the world. Humanitarian supply ...
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China introduces a new strain of coronavirus as a causative of a new respiratory disease after several people contracted an unusual pneumonia in December 2019. The World Health Organization stated that the outbreak of the virus resulted in public health emergencies around the world. Humanitarian supply chain management is concerned with managing the efficient flow of aid materials, information and services and aim to reduce the impact of disaster on human lives. In this paper, provides a ranking for key resources in the humanitarian supply chain in the emergency department of Iranian hospital using hybrid decision-making method under COVID-19 conditions. According to the obtain results, nurses in RK 1, receptionists RK 2, general surgeon RK 3, heart residents RK 4 and pulmonologist RK 5. Hybrid decision-making method in this paper is an invaluable contribution to the emergency department and medical managers for evaluates of current situation Emergency Department when crisis occur.
Decision analysis and methods
Vahid Mottaghi; Mahdi Esmaeili; Ghasem Ali Bazaee; Mohammadali Afshar Kazemi
Abstract
With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news ...
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With the increase of news on social networks, a way to identify fake news has become an essential matter. Classification is a fundamental task in natural language processing (NLP). Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of fake news classification. In this paper, new baseline models were studied for fake news classification using CNN. In these models, documents are fed to the network as a 3-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the texts. Besides, analyzing adjacent sentences allows extracting additional features. The proposed models were compared with the state-of-the-art models using a collection of real and fake news extracted from Twitter about covid-19, and the fusion layer was used as the decision layer in selecting the best feature. The results showed that the proposed models had better performance, particularly in these documents, and the results were obtained with 97.33% accuracy for classification on Covid-19 after reviewing the evaluation criteria of the proposed decision system model.
