This study included a survey and review of the scientific names of the marsh insects (aquatic and surrounding it) for the purpose of unifying and updating the database.
The survey reveals 109 species under 77 genera that belong to 32 families and 7 orders as follow: Coleoptera (44 species), Diptera (7 species) Ephemeroptera (2 species), Hemiptera (14 species), Hymenoptera (11 species), Lepidoptera (2 species) and Odonata with 29 species.
Information of specimens' collection for each species, synonyms and geographical distribution were provided.

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

This study has been carried out in the Station of Poultry Researches which is affiliated to the General Office of Agricultural Researches / Ministry of Agriculture during the period from 01/04/2018 to 14/05/2018 there are 300 one day old chick of type (Ross 308) used in this study, and has been fed on diets which green tea powder (Camellia sinensis) has been added to it with the levels 0.5 , 1 , 1.5 , 2 g/kg as a feed for the treatments T 2 , T 3 , T 4 and T 5 respectively and compared to the control treatment T 1 which is devoid of addition, every treatment included three replicates each one has 20 birds in order to study the effect of adding a various levels of green tea powder (Camellia sinensis) to the diet on the productive performance