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Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.

Arabic language processing presents significant challenges due to its complex linguistic patterns and shortage of resources. This study describes MSG-ATS, a new technique to abstractive text summarization in Arabic that aims to overcome these issues. The key challenge is producing coherent and high-quality summaries given the Arabic language’s rich syntactic, semantic, and contextual elements. Traditional approaches, such as word2vec, frequently fail to capture these subtleties well. MSG-ATS uses multilevel semantic graphs and deep learning techniques to create a more thorough representation of Arabic text. This approach improves traditional text generation and embedding approaches by collecting syntactic, semantic, and contextual information fully. MSG-ATS uses a deep neural network to create high-quality summaries that are coherent and contextually appropriate. To verify MSG-ATS, we performed rigorous assessments that compared its performance to word2vec, a fundamental word embedding approach. These assessments employed a unique dataset created expressly for this study and included automated assessment using the ROUGE measure. The results are compelling: MSG-ATS outperformed the baseline model by 42.4% in precision, 23.8% in recall, and 38.3% overall. The outcomes of this study highlight MSG-ATS’s potential to considerably increase Arabic text summarization by providing a strong framework that solves the constraints of existing models while also laying the groundwork for future developments in the area.

The domain of the digestive system is prone to severe chronic disease in the form of liver cirrhosis, which is currently a leading cause of mortality. This article presents a new intelligent system for predicting the severity of liver fibrosis in patients with chronic viral hepatitis C. The proposed system is based on the inference capabilities of fuzzy ontology and operates on semantic rule-based techniques. A fuzzy decision tree technique was employed to generate the ontology rule base using a dataset of real fibrosis cases from the Mansoura University Hospital, Egypt. These rules were then encoded into a set of fuzzy semantic rules using the fuzzy description logic format. To evaluate the system’s effectiveness, the proposed ontology was then tested on 47 chronic HCV cases, with an attempt made to see if this correctly diagnosed the patients’ conditions. The performance of the proposed system was compared with that of the now-standard Mamdani fuzzy inference system; while the latter achieved an accuracy of 95.7/%, the proposed fuzzy ontology-based system demonstrated higher performance, with 97.8% accuracy. Furthermore, the proposed system also supports semantic interoperability between clinical decision support systems and electronic health record ecosystems. The positive impacts of this system on the correct prediction of liver fibrosis severity thus suggest that it has the potential to assist medical professionals in diagnosing and treating this dangerous disease.