Breast cancer remains the most prevalent malignancy among women worldwide. The timely detection of this cancer type is critical for improving survival outcomes. Despite advancements, mammogram classification using deep learning strategies still faces challenges. These include inter-view feature inconsistency, loss of diagnostic details, and limited interpretability. In order to address these issues, MammoFusion-Net, a dual-branch deep learning framework, is proposed for mammogram-based breast cancer classification. Using residual convolutional streams, the framework processes craniocaudal (CC) and mediolateral oblique (MLO) views independently. This supports preservation of view-specific anatomical information. In the proposed framework, a Gates Cross-View Fusion mechanism adaptively integrates features across views. As a result of experimental analysis, the proposed framework achieved 92.116 % (VinDr-Mammo dataset) and 95.556 % (INBreast dataset) of improved classification performance.•Employs a dual-branch architecture to independently process CC and MLO views using residual convolutional streams.•Integrates Gated Cross-View Fusion and attention mechanisms adaptively and refines multi-view features for stronger discrimination.•Demonstrates the explainability of the model through Grad-CAM visualizations that highlight lesion-relevant regions.

The Satellite-Terrestrial Integrated Networks (STIN) were emerged as a key architectural pattern for attaining seamless, global and resilient wireless connectivity by adding extensive coverage of satellite systems with the high capacity and low latency of terrestrial networks. In spite of their advantages, STINs face significant challenges arising from heterogeneous link qualities, dynamic network topologies, long propagation delay and highly variable channel conditions which may complicate reliable and adaptable communication. The accurate and timely assessment of link quality is essential to enable effective resource management, adaptive modulation and coding and robust network control in space-ground integrated environments. In this study, AI-based link quality classification model for STINs based on a stacked ensemble learning architecture. This model combines multiple lightweight machine learning classifiers and a meta-level learner to capture complex non-linear relationships among satellite orbital parameters, spatial characteristics, and link dynamics. The framework categorizes satellite-terrestrial links into three operational states as Good, Moderate and Poor, which provides actionable intelligence for cross-layer resource allocation and adaptive communication strategies. The extensive experimental evaluation demonstrates that the proposed work attains 96.79% classification accuracy and 96.88% macro averaged F1-score where Decision tree as 85% and Machine Learning Based attain 88%. This indicates highly balanced and robust performance across all link classes. The confusion matrix analysis reveals that the misclassification occurs only between adjacent link quality states with no critical misclassifications between good and poor links. This ensures high reliability for operational decision-making. When compared to single-model baselines, the proposed approach increases prediction stability and robustness under heterogeneous and dynamic STIN conditions. The results confirmed the machine learning-assisted link quality intelligence can serve as a practical and efficient enabler for dynamic resource management in STIN. The model is computationally effective, scalable and readily deployable within next-generation STIN control planes which supports reliable communication for broadband access, emergency services and IoT applications.