Landslide detection and segmentation are critical for disaster risk assessment and management. However, achieving accurate segmentation remains challenging due to the complex nature of landslide terrains and the limited availability of high-quality labeled datasets. This paper proposes an enhanced U-Net++ model for semantic segmentation of landslides in the Wenchuan region using the CAS Landslide Dataset. The proposed model integrates multi-scale feature extraction and attention mechanisms to enhance segmentation accuracy and robustness. The experimental results demonstrate that ASK-UNet++ outperforms traditional methods, achieving a mean intersection over union (mIoU) of 97.53%, a Dice coefficient of 98.27%, and an overall accuracy of 96.04%. These findings highlight the potential of the proposed approach for improving landslide monitoring and disaster response strategies.

Software-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynamically apply and enforce security and traffic policies. The shift in architecture offers numerous advantages such as increased flexibility, scalability, and improved network management but also introduces new and notable security challenges such as Distributed Denial-of-Service (DDoS) attacks. Such attacks focus on affecting the target with malicious traffic and even short-lived DDoS incidents can drastically impact the entire network’s stability, performance and availability. This comprehensive review paper provides a detailed investigation of SDN principles, the nature of DDoS threats in such environments and the strategies used to detect/mitigate these attacks. It provides novelty by offering an in-depth categorization of state-of-the-art detection techniques, utilizing machine learning, deep learning, and federated learning in domain-specific and general-purpose SDN scenarios. Each method is analyzed for its effectiveness. The paper further evaluates the strengths and weaknesses of these techniques, highlighting their applicability in different SDN contexts. In addition, the paper outlines the key performance metrics used in evaluating these detection mechanisms. Moreover, the novelty of the study is classifying the datasets commonly used for training and validating DDoS detection models into two major categories: legacy-compatible datasets that are adapted from traditional network environments, and SDN-contextual datasets that are specifically generated to reflect the characteristics of modern SDN systems. Finally, the paper suggests a few directions for future research. These include enhancing the robustness of detection models, integrating privacy-preserving techniques in collaborative learning, and developing more comprehensive and realistic SDN-specific datasets to improve the strength of SDN infrastructures against DDoS threats.

Landslide hazard assessment increasingly demands the joint analysis of heterogeneous remote sensing data; however, automating this process remains difficult due to the pronounced resolution and texture discrepancies existing between satellite and aerial sensors. To address these limitations, this study proposes a robust segmentation framework capable of extracting sensor-robust representations. The framework leverages a DINOv3 transformer encoder and exploits representations from multiple transformer layers to capture complementary visual information, ranging from fine-grained surface textures to global semantic contexts, overcoming the receptive field constraints of conventional CNNs. Experiments on the Longxi satellite dataset achieve a Dice coefficient of 0.96 and an IoU of 0.938, and experiments on the Longxi UAV dataset achieve a Dice coefficient of 0.965 and an IoU of 0.941. These results show consistent segmentation performance on both the Longxi satellite and UAV datasets, despite differences in spatial resolution and surface appearance between acquisition platforms.

Unmanned marine vehicles (UMVs) are one of the most crucial components of the underwater communication and surveillance system in the marine and oceanographic environment. This research introduces a novel approach to stabilize the connection in an unstable underwater environment by inheriting features of communication velocities. The innovative proposed technique effectively divides the entire communication underwater by distributing the burden and ensuring trouble‐free and efficient communication and surveillance. In existing approaches, troubleshooting communication issues often results in delays or slow performance. However, the proposed method focuses on establishing an efficient communication link to increase the throughput of the deployed underwater network. To achieve this, we have implemented two different communication channels, acoustic and wireless optical, that provide redundant pathways for communication, which enhances reliability and resilience. In the event of a denial‐of‐service attack or failure in one communication channel, the other channel becomes the primary means of communication, ensuring uninterrupted data transmission. We have used an attack prediction machine‐learning model to further improve system security. Random forest (RF), naïve Bayes classifiers, and machine learning methods like support vector machines (SVM), multilayer perceptrons (MLP), and k‐nearest neighbors (KNN) are used to forecast the likelihood of an attack. To stop unwanted access or malicious activity, the associated communication channel is shut when a model shows a high probability of an attack. The efficiency of our recommended control strategy is confirmed by a simulation study of the proposed networked UMVs system. The suggested system delivers improved robustness and secure communication for real‐time situations in the underwater environment by using machine learning‐based attack prediction, redundant communication channels, and varied velocities to stabilize the connection. SVM achieves high accuracy at 98%. MLP is not far behind, with a 96% accuracy rate. The higher accuracy of SVM and MLP in predicting DoS assaults in UMVs is highlighted by the lesser accuracies of 66.3.

Women exhibit marked physiological transformations in pregnancy, mandating regular and holistic assessment. Maternal and fetal vitality is governed by a spectrum of clinical, demographic, and lifestyle factors throughout this critical period. The existing maternal health monitoring techniques lack precision in assessing pregnancy-related risks, often leading to late interventions and adverse outcomes. Accurate and timely risk prediction is crucial to avoid miscarriages. This research proposes a deep learning framework for personalized pregnancy risk prediction using the NFHS-5 dataset, and class imbalance is addressed through a hybrid NearMiss-SMOTE approach. Fifty-one primary features are selected via the LASSO to refine the dataset and enhance model interpretability and efficiency. The framework integrates a multimodal model (NFHS-5, fetal plane images, and EHG time series) along with two core architectures. ResRNN-Net further combines Bi-LSTM, CNNs, and attention mechanisms to capture sequential dependencies. MultiScaleFusion-Net leverages GRU and multiscale convolutions for effective feature extraction. Additionally, TabNet and MLP models are explored to compare interpretability and computational efficiency. SHAP and Grad-CAM are used to ensure transparency and explainability, offering both feature importance and visual explanations of predictions. The proposed models are trained using 5-fold stratified cross-validation and evaluated with metrics including accuracy, precision, recall, F1-score, and ROC–AUC. The results demonstrate that MultiScaleFusion-Net balances accuracy and computational efficiency, making it suitable for real-time clinical deployment, while ResRNN-Net achieves higher precision at a slight computational cost. Performance comparisons with baseline machine learning models confirm the superiority of deep learning approaches, achieving over 80% accuracy in pregnancy complication prediction.

Semantic segmentation of very-high-resolution remote sensing imagery benefits from combining RGB appearance with Digital Surface Model (DSM) height information, especially in urban scenes where spectrally similar objects often differ in elevation. On the ISPRS Vaihingen and Potsdam benchmarks, BATFNet achieves mIoU scores of 84.06% and 85.31%, respectively, outperforming representative RGB–DSM fusion baselines on most land-cover categories. BATFNet is a supervised boundary-aware Transformer fusion network that uses DSM-derived edge priors to guide bidirectional cross-modal attention and decoder refinement. With a dual-branch ResNet-50 backbone for modality-specific feature extraction, the proposed framework effectively integrates RGB and DSM information while recovering fine spatial details. These results show that exploiting DSM-derived structural cues improves boundary delineation and reduces confusion among spectrally similar urban classes.