Mohammad Amin Fazli

Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli et al.

The availability and interactive nature of social media have made them the primary source of news around the globe. The popularity of social media tempts criminals to pursue their immoral intentions by producing and disseminating fake news using seductive text and misleading images. Therefore, verifying social media news and spotting fakes is crucial. This work aims to analyze multi-modal features from texts and images in social media for detecting fake news. We propose a Fake News Revealer (FNR) method that utilizes transform learning to extract contextual and semantic features and contrastive loss to determine the similarity between image and text. We applied FNR on two real social media datasets. The results show the proposed method achieves higher accuracies in detecting fake news compared to the previous works.

Hossein Rajaby Faghihi, Mohammad Amin Fazli, Jafar Habibi

The success of smart environments largely depends on their smartness of understanding the environments' ongoing situations. Accordingly, this task is an essence to smart environment central processors. Obtaining knowledge from the environment is often through sensors, and the response to a particular circumstance is offered by actuators. This can be improved by getting user feedback, and capturing environmental changes. Machine learning techniques and semantic reasoning tools are widely used in this area to accomplish the goal of interpretation. In this paper, we have proposed a hybrid approach utilizing both machine learning and semantic reasoning tools to derive a better understanding from sensors. This method uses situation templates jointly with a decision tree to adapt the system knowledge to the environment. To test this approach we have used a simulation process which has resulted in a better precision for detecting situations in an ongoing environment involving living agents while capturing its dynamic nature.