Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions. These images represent vast datasets that are increasingly harnessed by deep learning for high-performance image processing and classification tasks. In our study, we focus on classifying brain tumors—such as glioma, meningioma, and pituitary tumors—using the U-Net architecture applied to MRI scans. Additionally, we explore the effectiveness of convolutional neural networks including Inception-V3, EfficientNetB4, and VGG19, augmented through transfer learning techniques. Evaluation metrics such as F-score, recall, precision, and accuracy were employed to assess model performance. The U-Net segmentation architecture, emerged as the top performer, achieving an accuracy of 98.56%, along with an F-score of 99%, an area under the curve of 99.8%, and recall and precision rates of 99%. This study demonstrates the effectiveness of U-Net, a convolutional neural network architecture, for accurate brain tumor segmentation in early detection and treatment planning. Achieving an accuracy of 96.01% in cross-dataset validation with an external cohort, U-Net exhibited robust performance across diverse clinical scenarios. Our findings highlight the potential of U-Net and transfer learning in enhancing diagnostic accuracy and informing clinical decision-making in neuroimaging, ultimately improving patient care and outcomes.

In this paper, we present a physics-based analytical model of GNR FET, which allows for the evaluation of GNR FET performance including the effects of line-edge roughness as its practical specific non-ideality. The line-edge roughness is modeled in edge-enhanced band-to-band-tunneling and localization regimes, and then verified for various roughness amplitudes. Corresponding to these two regimes, the off-current is initially increased, then decreased; while, on the other hand, the on-current is continuously decreased by increasing the roughness amplitude.

This paper presents a comparative study of sampling methods within the FedHome framework, designed for personalized in-home health monitoring. FedHome leverages federated learning (FL) and generative convolutional autoencoders (GCAE) to train models on decentralized edge devices while prioritizing data privacy. A notable challenge in this domain is the class imbalance in health data, where critical events such as falls are underrepresented, adversely affecting model performance. To address this, the research evaluates six oversampling techniques using Stratified K-fold cross-validation: SMOTE, Borderline-SMOTE, Random OverSampler, SMOTE-Tomek, SVM-SMOTE, and SMOTE-ENN. These methods are tested on FedHome's public implementation over 200 training rounds with and without stratified K-fold cross-validation. The findings indicate that SMOTE-ENN achieves the most consistent test accuracy, with a standard deviation range of 0.0167-0.0176, demonstrating stable performance compared to other samplers. In contrast, SMOTE and SVM-SMOTE exhibit higher variability in performance, as reflected by their wider standard deviation ranges of 0.0157-0.0180 and 0.0155-0.0180, respectively. Similarly, the Random OverSampler method shows a significant deviation range of 0.0155-0.0176. SMOTE-Tomek, with a deviation range of 0.0160-0.0175, also shows greater stability but not as much as SMOTE-ENN. This finding highlights the potential of SMOTE-ENN to enhance the reliability and accuracy of personalized health monitoring systems within the FedHome framework.

Synthetic Aperture Radar (SAR) imagery plays a critical role in flood mapping due to its ability to capture data under all-weather and day-and-night conditions. However, existing SAR segmentation methods, including the state-of-the-art HisSegNet [1], face challenges such as limited generalization, insufficient utilization of SAR-specific features, and suboptimal performance on diverse datasets. To address these limitations, we propose Enhanced-HisSegNet, a multimodal fusion strategy that builds upon HisSegNet by integrating learnable histogram layers tailored for SAR data with Active Contour Models (ACMs) for precise boundary refinement. These components are embedded into fine-tuned Deep Segmentation Neural Networks (DSNNs) to improve segmentation accuracy. Our model was evaluated on real SAR datasets, employing cross-dataset validation for robustness. Experimental results demonstrate significant performance gains, with up to 10% improvement in Intersection over Union (IoU) - a key metric that measures segmentation accuracy by computing the ratio of intersection to union between predicted and ground truth regions - on internal datasets and 4% on external datasets, showcasing enhanced accuracy, robustness, and applicability. The code for this work is available at https://github.com/Mohsena1990/Enhanced-HistSegNet.