Introduction; The protection of patient privacy through medical image de-identification stands as a vital yet complicated healthcare challenge which demands both accurate diagnosis and privacy protection. Deep learning techniques now provide superior methods to enhance medical images which suffer from acquisition noise and low-resolution degradation. The research develops a Generative Adversarial Network (GAN) architecture to create a deep-learning solution which enhances medical images while removing identifying information.Objective; The proposed method uses adversarial learning to eliminate noise while restoring detailed high-resolution content from low-quality medical images.Method; The research team analysed medical images through data analysis of their dataset. The GAN model received training and validation through experiments that compared its performance against established demo denoising and super-resolution techniques to assess its overall performance. The fundamental technology shows promise for future medical applications because it can enhance image quality to diagnose and treat multiple diseases. Medical image analysis requires images with diverse detailed features for proper evaluation.Results; The proposed model demonstrates successful background noise reduction and improved image clarity with preserved diagnostic elements according to the obtained results. The proposed method achieved better results in PSNR and SSIM metrics than baseline models which proved its ability to restore vital diagnostic details. Conclusion; The research introduces an innovative GAN-based system which delivers superior medical image quality while maintaining patient information confidentiality during de-identification processes. The method shows promise to create efficient and economical diagnostic processes through its ability to analyze poor-quality medical images.

Introduction: Adaptive e-Learning denotes a collection of methodologies aimed at providing online learn-ers with a personalized and distinctive experience, ultimately intended to enhance their performance. Adaptive e-Learning operates on the premise that each learner possesses dis-tinct characteristics, including varied backgrounds, educational requirements, and learning preferences. Objectives: The aim of Adaptive e-Learning is to identify individual differences and convert them into pertinent information and training methodologies tailored for each student. This paper explores the used of machine learning (ML) and deep leaning techniques in crafting personalized learning experiences within adaptive e-learning systems, especially estimated the learning style. Methods: It highlights key algorithms employed for learner modeling, recommendation, assessment, and improved student outcomes by focused on the style of learning for the students. First step, we used an educational dataset that are related to the learning style for 1,210 students. Then, the preprocessing steps were applied on the dataset like checking missing values, and turning object columns type to categorical for easing the transformation process. Results: The deep learning techniques that were used are deep neural network (DNN), while the ML techniques are Random Forest, XGBoost, AdaBoost, and Logistic Regression. To assess these algorithms, the accuracy, precision, recall, and f1-score. The finding of this paper for all the algorithms are as follows: DNN (91, 89, 90, 90), RF (83, 83, 83, 83), LR (89, 90, 90, 90), XGB (83, 83, 83, 83), and Ada (86, 87, 86, 86). We have shown that the DNN gave the best prediction of learning style compared with the others. Conclusions: investigating the impact of feature extraction and selection, as highlighted in previous studies, could contribute to refining the accuracy and effectiveness of the system.

Introduction: Medical imaging serves as a crucial tool for disease diagnosis but current image analysis techniques fail to handle noisy data and insufficient annotations and different imaging modalities. Deep learning techniques have transformed medical imaging but achieving high diagnostic accuracy alongside computational efficiency remains a key challenge in clinical deployment. Objective: The research proposes a single deep learning system which combines CNNs with RNNs and GANs to enhance automated disease detection from medical images through improved accuracy, better interpretability and faster processing times.Method: The proposed Transformer-guided hybrid model uses CNNs to extract spatial features and RNNs to detect temporal patterns while GANs perform data augmentation and anomaly detection. Use consistent passive or active voice. The model was trained, validated on multimodal datasets and subsequently evaluated against ten baseline models, including SVM, transfer learning, and attention-based architectures. The evaluation metrics consisted of accuracy and precision and sensitivity and ROC-AUC.Results: The integrated framework achieved superior diagnostic performance with 90% accuracy, 88% precision, 86% sensitivity and 0.95 ROC-AUC which outperformed all baseline models. The system delivered achieved faster processing without sacrificing diagnostic accuracy across imaging modalities without compromising its diagnostic accuracy for different imaging techniques. Conclusions: The research developed an AI diagnostic system which uses CNN, RNN and GAN technologies to achieve efficient and ethical medical image analysis. The system enhances precision and speed while ensuring patient data security and transparent clinical reporting, enabling scalable AI-driven diagnostics.

Introduction; The detection of motion-based cardiac abnormalities at an early stage proves difficult because individual systems fail to measure how motion affects depth and structural changes. A multimodal spatiotemporal system would enhance the accuracy of medical diagnoses. Objective; The research aims to create a real-time system which unites cardiac video data with range/depth information to detect cardiac conditions at an early stage. Method; The system operates through independent encoders which join their data streams through a gated fusion module. The system performs denoising operations followed by statistical normalization and geometric transformation of the input data. The system uses beat-level temporal attention to identify essential time segments for clinical evaluation. The research evaluated system performance through comparison with video transformers and traditional temporal analysis methods. Result; The model produced F1 reached 0.945 while AUROC reached 0.9978 and the model achieved sensitivity at 0.950 and specificity at 0.940 and precision at 0.940 and AUPRC at 0.972. The system demonstrated excellent calibration performance through its ECE and Brier values which approached perfect results (slope ≈ 1.01, ≈ 0). The system produced useful screening results when using 10% and 20% thresholds which produced 0.142 and 0.118 respectively. The system performed real-time processing at 4.9 GFLOPs while maintaining a processing time of ~98 ms. Conclusion; The combination of intensity dynamics with depth-derived geometry allows for accurate real-time cardiac prediction with precise calibration. The proposed method delivers superior results than single-signal systems and conventional temporal methods which makes it a useful advancement for early detection and point-of-care cardiology.

American scientific publishing group

This paper describes the design of a secure and sustainable communication framework that is specifically targeted for interactive mobile learning platforms and the data of the users are protected through effectively applying lightweight cryptographic methods, characterizing an adaptive risk based authentication model for improving the access control, and utilizing a deep learning based encrypted traffic classifier for real time anomaly detection and the supporting energy-aware strategies of the system include intelligent task offloading and bandwidth-adaptive content delivery so these strategies address energy constraints in mobile devices and to reduce latency and improve responsiveness, the system operates smoothly in Mobile Edge Computing (MEC) environments and the Performance results from a simulated 5G network are strong: authentication accuracy reached 96.4% and the F1 score for encrypted traffic detection was 96.1%, and mobile energy consumption decreased by up to 27% and the proposed framework offers a comprehensive and efficient solution compared to existing ones, ensuring security, user adaptability, and sustainability. It is well suited for the next generation of mobile learning applications in smart educational ecosystems.