While human brains have the ability to distinguish face characteristics, the use of advanced technology and artificial intelligence blurs the difference between actual and modified images. The evolution of digital editing applications has led to the fabrication of very lifelike false faces, making it harder for humans to discriminate between real and made ones. Because of this, techniques like deep learning are being used increasingly to distinguish between real and artificial faces,producing more consistent and accurate results. In order to detect fraudulent faces, This paper introduces a pioneering hybrid deep learning model, which merges the capabilities of Generative Adversarial Networks (GANs) and the Residual Neural Network (RESNET) architecture, aimed at detecting fake faces. By integrating GANs’ generative strength with RESNET’s discriminative abilities, the proposed model offers a novel approach to discerning real from artificial faces. Through a comparative analysis, the performance of the hybrid model is evaluated against established pre-trained models such as VGG16 and RESNET 50. Results demonstrate the superior effectiveness of the hybrid model in accurately detecting fake faces, marking a notable advancement in facial image recognition and authentication. The findings on a benchmark dataset show that the proposed model obtains outstanding performance measures, including precision 0.79, recall 0.88, F1-score 0.83, accuracy 0.83, and ROC AUC Score 0.825. The study’s conclusions highlight the hybrid model’s strong performance in identifying fake faces, especially when it comes to accuracy, precision, and memory economy. By combining the generative capacity of GANs with the discriminative capabilities of RESNET, this solves the problems caused by more complex fake face generation approaches.With significant potential for use in identity verification, social media content moderation, cybersecurity, and other areas, the study seeks to advance the field of false face identification. In these situations, being able to accurately discriminate between real and altered faces is crucial. Notably, our suggested model adds Channel-Wise Attention Mechanisms to RESNET50 at the feature extraction phase, which increases its effectiveness and boosts its overall performance.

Accurate stock price forecasting is essential for making smart investing choices. In the context of the Egyptian stock market, this study examines the predictive capabilities of several machine learning and deep learning models for stock price prediction. Five different datasets with historical stock price information from significant Egyptian companies are used in the study methods such as Random Forest, Linear Regression, LSTM, and Bi-LSTM which were employed and evaluated using performance metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared. Among these, the Bi-LSTM model consistently delivered the most accurate price forecasts due to its superior ability to capture complex patterns and relationships inherent in stock market data. Additionally, statistical models ARIMA and GARCH were implemented for comparative purposes, but the machine learning and deep learning models outperformed these traditional approaches. This study contributes to a deeper understanding of stock price behavior in the Egyptian market, providing valuable insights for financial analysts and investors.

Identifying changes in satellite images is vital for tasks like tracking land cover and land use, evaluating disaster impacts, and conducting military surveillance. Although conventional techniques for detecting changes in multispectral remote sensing data are commonly applied, they often fail to meet the requirements for reliability and precision. Recently, deep learning methods have emerged, providing more accurate and effective solutions for monitoring environmental transformations and urban expansion in satellite imagery. This paper introduces TransSiamUNet, a deep learning architecture that combines Siamese networks, U-Net segmentation, and Vision Transformers (ViT) for high-precision change detection. The model processes paired Sentinel-2 images via a tailored preprocessing pipeline and integrates local and global feature extraction for pixel-level change segmentation. On the OSCD benchmark, TransSiamUNet achieves an accuracy of 0.94, surpassing the Siamese network (0.86), U-Net (0.84), and Siamese+U-Net hybrid (0.91). These results demonstrate the model’s superior capability in detecting fine-grained urban and environmental changes, highlighting its suitability for real-world remote sensing applications.

Background: Intestinal parasitic infection is widespread worldwide and a serious public health problem. It is responsible for enormous morbidity and mortality around the world, particularly in developing countries. Artificial intelligence can create therapeutic suggestions that are individually tailored to each client’s unique traits. This study aimed to develop an intelligent system for mothers to provide future recommendations for more effective interventions to combat intestinal parasitic infections. Methods: A quasi-experimental research design (pre/post-test) was utilized to achieve the aim of the current study. The study was conducted at Dar Al Salam Family Health Center in Cairo Governorate, Egypt. A purposive sample of 200 mothers was included in this study. Two tools were used for data collection: First tool: Structured knowledge questionnaire that has four parts: Part I: Demographic data; Part II: Family history for intestinal parasitic infection; Part III: Home environment; Part IV: Mothers’ knowledge regarding intestinal parasitic infection. Second tool: Mothers’ reported preventive measures checklist. Results: There was a highly significant statistical difference in the mothers’ total knowledge level and total preventive measures regarding intestinal parasitic infection between pre and posttest. Moreover, there was highly significant statistical positive correlation between mothers’ total knowledge and total preventive measures at pre and post-test. The integration of large language model driven insights, quick engineering, and tailored treatments demonstrated favorable outcomes in enhancing mothers’ cleanliness routines. The novelty of this study lies in integrating K-Means clustering, large language models (GPT-4o), and prompt engineering to generate culturally tailored and behavior-specific preventive recommendations for mothers regarding intestinal parasitic infections. Conclusions: Preventive educational programs have a significant positive effect on improving mothers’ knowledge and preventive measures to combat intestinal parasitic infections. This study effectively underscores the significance of artificial intelligence-driven analysis and large language models in producing tailored suggestions to enhance mothers’ hygiene routines.

Background: Diabetes mellitus is a global health challenge, especially among homeless people. Early prediction of diabetes can reduce treatment costs and improve interventions. This study aimed to identify predictors of diabetes among homeless adults by utilizing artificial intelligence and providing recommendations for diabetes prevention. Methods: A case-control study of 150 homeless adults in Giza, Egypt (99 diabetes cases and 51 controls), analyzed 43 variables collected through interviews and physiological measures, with missing data imputed. Feature selection using recursive feature elimination and univariate and correlation analyses reduced the predictors to 13 variables. The class imbalance was addressed using synthetic minority over-sampling on the training set. Six models and a stacking ensemble with XGBoost as a meta-learner were evaluated using 5-fold cross-validation and performance metrics, including the accuracy, precision, recall, F1-score, and AUC-ROC. Results: The key predictors included BMI, systolic blood pressure, triceps skinfold thickness, waist circumference, lifestyle factors, comorbidities, diastolic blood pressure, age, medication adherence, educational level, marital status, duration of residence, and diabetes knowledge. Individual classifiers achieved a moderate performance (accuracy: 56.7–70.0%, F1-score: 0.686–0.781). The stacking ensemble substantially outperformed individual models, achieving a 95.45% accuracy, a 100% precision, a 93.75% recall, a 0.968 F1-score, and a 0.979 AUC-ROC on the test set. Conclusions: Machine learning models can reliably predict diabetes. The proposed hybrid stacking model outperformed conventional classifiers in terms of the prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes.